wip

script to render templates
win: warn if ggml-base detected in PATH (#13289 )
2025-12-02 12:17:36 -08:00 · 2025-12-01 18:03:04 -08:00 · 2025-12-01 15:36:47 -08:00 · 2025-12-01 15:10:16 -08:00 · 2025-12-01 12:48:16 -08:00 · 2025-11-29 23:46:10 -05:00
58 changed files with 4429 additions and 860 deletions
--- a/.gitattributes
+++ b/.gitattributes
@@ -15,8 +15,12 @@ ml/backend/**/*.cu linguist-vendored
 ml/backend/**/*.cuh linguist-vendored
 ml/backend/**/*.m linguist-vendored
 ml/backend/**/*.metal linguist-vendored
+ml/backend/**/*.comp linguist-vendored
+ml/backend/**/*.glsl linguist-vendored
 ml/backend/**/CMakeLists.txt linguist-vendored

+app/webview linguist-vendored
+
 llama/build-info.cpp linguist-generated
 ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-embed.s linguist-generated

--- a/api/client.go
+++ b/api/client.go
@@ -226,7 +226,14 @@ func (c *Client) stream(ctx context.Context, method, path string, data any, fn f

 		bts := scanner.Bytes()
 		if err := json.Unmarshal(bts, &errorResponse); err != nil {
-			return fmt.Errorf("unmarshal: %w", err)
+			if response.StatusCode >= http.StatusBadRequest {
+				return StatusError{
+					StatusCode:   response.StatusCode,
+					Status:       response.Status,
+					ErrorMessage: string(bts),
+				}
+			}
+			return errors.New(string(bts))
 		}

 		if response.StatusCode == http.StatusUnauthorized {
--- a/api/client_test.go
+++ b/api/client_test.go
@@ -55,6 +55,7 @@ func TestClientFromEnvironment(t *testing.T) {
 type testError struct {
 	message    string
 	statusCode int
+	raw        bool // if true, write message as-is instead of JSON encoding
 }

 func (e testError) Error() string {
@@ -111,6 +112,20 @@ func TestClientStream(t *testing.T) {
 				},
 			},
 		},
+		{
+			name: "plain text error response",
+			responses: []any{
+				"internal server error",
+			},
+			wantErr: "internal server error",
+		},
+		{
+			name: "HTML error page",
+			responses: []any{
+				"<html><body>404 Not Found</body></html>",
+			},
+			wantErr: "404 Not Found",
+		},
 	}

 	for _, tc := range testCases {
@@ -135,6 +150,12 @@ func TestClientStream(t *testing.T) {
 						return
 					}

+					if str, ok := resp.(string); ok {
+						fmt.Fprintln(w, str)
+						flusher.Flush()
+						continue
+					}
+
 					if err := json.NewEncoder(w).Encode(resp); err != nil {
 						t.Fatalf("failed to encode response: %v", err)
 					}
@@ -173,9 +194,10 @@ func TestClientStream(t *testing.T) {

 func TestClientDo(t *testing.T) {
 	testCases := []struct {
-		name     string
-		response any
-		wantErr  string
+		name           string
+		response       any
+		wantErr        string
+		wantStatusCode int
 	}{
 		{
 			name: "immediate error response",
@@ -183,7 +205,8 @@ func TestClientDo(t *testing.T) {
 				message:    "test error message",
 				statusCode: http.StatusBadRequest,
 			},
-			wantErr: "test error message",
+			wantErr:        "test error message",
+			wantStatusCode: http.StatusBadRequest,
 		},
 		{
 			name: "server error response",
@@ -191,7 +214,8 @@ func TestClientDo(t *testing.T) {
 				message:    "internal error",
 				statusCode: http.StatusInternalServerError,
 			},
-			wantErr: "internal error",
+			wantErr:        "internal error",
+			wantStatusCode: http.StatusInternalServerError,
 		},
 		{
 			name: "successful response",
@@ -203,6 +227,26 @@ func TestClientDo(t *testing.T) {
 				Success: true,
 			},
 		},
+		{
+			name: "plain text error response",
+			response: testError{
+				message:    "internal server error",
+				statusCode: http.StatusInternalServerError,
+				raw:        true,
+			},
+			wantErr:        "internal server error",
+			wantStatusCode: http.StatusInternalServerError,
+		},
+		{
+			name: "HTML error page",
+			response: testError{
+				message:    "<html><body>404 Not Found</body></html>",
+				statusCode: http.StatusNotFound,
+				raw:        true,
+			},
+			wantErr:        "<html><body>404 Not Found</body></html>",
+			wantStatusCode: http.StatusNotFound,
+		},
 	}

 	for _, tc := range testCases {
@@ -210,11 +254,16 @@ func TestClientDo(t *testing.T) {
 			ts := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
 				if errResp, ok := tc.response.(testError); ok {
 					w.WriteHeader(errResp.statusCode)
-					err := json.NewEncoder(w).Encode(map[string]string{
-						"error": errResp.message,
-					})
-					if err != nil {
-						t.Fatal("failed to encode error response:", err)
+					if !errResp.raw {
+						err := json.NewEncoder(w).Encode(map[string]string{
+							"error": errResp.message,
+						})
+						if err != nil {
+							t.Fatal("failed to encode error response:", err)
+						}
+					} else {
+						// Write raw message (simulates non-JSON error responses)
+						fmt.Fprint(w, errResp.message)
 					}
 					return
 				}
@@ -241,6 +290,15 @@ func TestClientDo(t *testing.T) {
 				if err.Error() != tc.wantErr {
 					t.Errorf("error message mismatch: got %q, want %q", err.Error(), tc.wantErr)
 				}
+				if tc.wantStatusCode != 0 {
+					if statusErr, ok := err.(StatusError); ok {
+						if statusErr.StatusCode != tc.wantStatusCode {
+							t.Errorf("status code mismatch: got %d, want %d", statusErr.StatusCode, tc.wantStatusCode)
+						}
+					} else {
+						t.Errorf("expected StatusError, got %T", err)
+					}
+				}
 				return
 			}

--- a/app/cmd/app/app.go
+++ b/app/cmd/app/app.go
@@ -397,8 +397,8 @@ func checkUserLoggedIn(uiServerPort int) bool {
 // handleConnectURLScheme fetches the connect URL and opens it in the browser
 func handleConnectURLScheme() {
 	if checkUserLoggedIn(uiServerPort) {
-		slog.Info("user is already logged in, opening settings instead")
-		sendUIRequestMessage("/")
+		slog.Info("user is already logged in, opening app instead")
+		showWindow(wv.webview.Window())
 		return
 	}

@@ -466,6 +466,8 @@ func handleURLSchemeInCurrentInstance(urlSchemeRequest string) {
 	if isConnect {
 		handleConnectURLScheme()
 	} else {
-		sendUIRequestMessage("/")
+		if wv.webview != nil {
+			showWindow(wv.webview.Window())
+		}
 	}
 }
--- a/app/cmd/app/app_darwin.m
+++ b/app/cmd/app/app_darwin.m
@@ -24,27 +24,14 @@ bool firstTimeRun,startHidden; // Set in run before initialization
    for (NSURL *url in urls) {
        if ([url.scheme isEqualToString:@"ollama"]) {
            NSString *path = url.path;
-            if (!path || [path isEqualToString:@""]) {
-                // For URLs like ollama://settings (without triple slash),
-                // the "settings" part is parsed as the host, not the path.
-                // We need to convert it to a path by prepending "/"
-                if (url.host && ![url.host isEqualToString:@""]) {
-                    path = [@"/" stringByAppendingString:url.host];
-                } else {
-                    path = @"/";
-                }
-            }
-            
-            if ([path isEqualToString:@"/connect"] || [url.host isEqualToString:@"connect"]) {
+
+            if (path && ([path isEqualToString:@"/connect"] || [url.host isEqualToString:@"connect"])) {
                // Special case: handle connect by opening browser instead of app
                handleConnectURL();
            } else {
                // Set app to be active and visible
                [NSApp setActivationPolicy:NSApplicationActivationPolicyRegular];
                [NSApp activateIgnoringOtherApps:YES];
-                
-                // Open the path with the UI
-                [self uiRequest:path];
            }
            
            break;
@@ -260,7 +247,7 @@ bool firstTimeRun,startHidden; // Set in run before initialization
 }

 - (void)openHelp:(id)sender {
-    NSURL *url = [NSURL URLWithString:@"https://github.com/ollama/ollama/tree/main/docs"];
+    NSURL *url = [NSURL URLWithString:@"https://docs.ollama.com/"];
    [[NSWorkspace sharedWorkspace] openURL:url];
 }

--- a/app/cmd/app/app_windows.go
+++ b/app/cmd/app/app_windows.go
@@ -147,7 +147,9 @@ func handleURLSchemeRequest(urlScheme string) {
 	if isConnect {
 		handleConnectURLScheme()
 	} else {
-		sendUIRequestMessage("/")
+		if wv.webview != nil {
+			showWindow(wv.webview.Window())
+		}
 	}
 }

--- a/cmd/chat_template/chat_template.py
+++ b/cmd/chat_template/chat_template.py
@@ -0,0 +1,625 @@
+#!/usr/bin/env python3
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#   "transformers>=4.57.0",
+#   "jinja2",
+#   "fastapi",
+#   "uvicorn",
+#   "pydantic",
+#   "requests",
+# ]
+# ///
+"""
+Chat Template Testing Tool
+
+Test HuggingFace chat templates against Ollama renderers.
+
+Usage:
+    # Run predefined test cases against a HuggingFace model
+    uv run cmd/chat_template/chat_template.py --model PrimeIntellect/INTELLECT-3
+
+    # Compare HuggingFace output with Ollama renderer
+    uv run cmd/chat_template/chat_template.py --model PrimeIntellect/INTELLECT-3 --ollama-model intellect3
+
+    # Start server for manual curl testing
+    uv run cmd/chat_template/chat_template.py --serve
+
+    # Show chat template for a model
+    uv run cmd/chat_template/chat_template.py --model PrimeIntellect/INTELLECT-3 --show-template
+"""
+
+import argparse
+import json
+import sys
+from typing import Any
+
+from transformers import AutoTokenizer
+
+
+TEST_CASES = [
+    {
+        "name": "basic_user_message",
+        "messages": [{"role": "user", "content": "Hello!"}],
+        "tools": None,
+    },
+    {
+        "name": "with_system_message",
+        "messages": [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Hello!"},
+        ],
+        "tools": None,
+    },
+    {
+        "name": "multi_turn_conversation",
+        "messages": [
+            {"role": "user", "content": "Hello"},
+            {"role": "assistant", "content": "Hi there!"},
+            {"role": "user", "content": "How are you?"},
+        ],
+        "tools": None,
+    },
+    {
+        "name": "with_tools",
+        "messages": [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "What is the weather?"},
+        ],
+        "tools": [
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "required": ["location"],
+                        "properties": {
+                            "location": {"type": "string", "description": "The city"}
+                        },
+                    },
+                },
+            }
+        ],
+    },
+    {
+        "name": "tool_call_and_response",
+        "messages": [
+            {"role": "user", "content": "What is the weather in SF?"},
+            {
+                "role": "assistant",
+                "content": "Let me check the weather.",
+                "tool_calls": [
+                    {
+                        "id": "call_1",
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": {"location": "San Francisco"},
+                        },
+                    }
+                ],
+            },
+            {"role": "tool", "content": '{"temperature": 68}', "tool_call_id": "call_1"},
+        ],
+        "tools": [
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "required": ["location"],
+                        "properties": {
+                            "location": {"type": "string", "description": "The city"}
+                        },
+                    },
+                },
+            }
+        ],
+    },
+    {
+        "name": "parallel_tool_calls",
+        "messages": [
+            {"role": "user", "content": "Get weather in SF and NYC"},
+            {
+                "role": "assistant",
+                "tool_calls": [
+                    {
+                        "id": "call_1",
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": {"location": "San Francisco"},
+                        },
+                    },
+                    {
+                        "id": "call_2",
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": {"location": "New York"},
+                        },
+                    },
+                ],
+            },
+            {"role": "tool", "content": '{"temperature": 68}', "tool_call_id": "call_1"},
+            {"role": "tool", "content": '{"temperature": 55}', "tool_call_id": "call_2"},
+        ],
+        "tools": [
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {"location": {"type": "string"}},
+                    },
+                },
+            }
+        ],
+    },
+    # Thinking tests
+    {
+        "name": "assistant_with_thinking",
+        "messages": [
+            {"role": "user", "content": "What is 2+2?"},
+            {
+                "role": "assistant",
+                "content": "The answer is 4.",
+                "thinking": "Let me calculate: 2 + 2 = 4. This is basic arithmetic.",
+            },
+            {"role": "user", "content": "And 3+3?"},
+        ],
+        "tools": None,
+    },
+    {
+        "name": "thinking_with_tool_call",
+        "messages": [
+            {"role": "user", "content": "What's the weather in Paris?"},
+            {
+                "role": "assistant",
+                "content": "I'll check the weather for you.",
+                "thinking": "The user wants to know the weather in Paris. I should call the get_weather function.",
+                "tool_calls": [
+                    {
+                        "id": "call_1",
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": {"location": "Paris"},
+                        },
+                    }
+                ],
+            },
+            {"role": "tool", "content": '{"temperature": 18, "condition": "cloudy"}', "tool_call_id": "call_1"},
+        ],
+        "tools": [
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_weather",
+                    "description": "Get current weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {"location": {"type": "string"}},
+                    },
+                },
+            }
+        ],
+    },
+    {
+        "name": "thinking_only_no_content",
+        "messages": [
+            {"role": "user", "content": "Think about this silently."},
+            {
+                "role": "assistant",
+                "content": "",  # HuggingFace requires content field
+                "thinking": "I'm thinking about this but won't respond with visible content.",
+            },
+            {"role": "user", "content": "What did you think?"},
+        ],
+        "tools": None,
+    },
+]
+
+# Cache for tokenizers
+_tokenizer_cache: dict[str, Any] = {}
+
+
+def get_tokenizer(model_name: str):
+    """Get or create tokenizer for the given model."""
+    if model_name not in _tokenizer_cache:
+        print(f"Loading tokenizer for {model_name}...", file=sys.stderr)
+        _tokenizer_cache[model_name] = AutoTokenizer.from_pretrained(model_name)
+    return _tokenizer_cache[model_name]
+
+
+def apply_template(
+    model: str,
+    messages: list[dict],
+    tools: list[dict] | None = None,
+) -> str:
+    """Apply HuggingFace chat template to messages."""
+    tokenizer = get_tokenizer(model)
+
+    if tools:
+        return tokenizer.apply_chat_template(
+            messages,
+            tools=tools,
+            tokenize=False,
+            add_generation_prompt=True,
+        )
+    else:
+        return tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True,
+        )
+
+
+def get_ollama_prompt(
+    ollama_model: str,
+    messages: list[dict],
+    tools: list[dict] | None = None,
+    ollama_host: str = "http://localhost:11434",
+) -> str | None:
+    """Get rendered prompt from Ollama using debug_render_only."""
+    import requests
+
+    # Convert messages to Ollama format
+    ollama_messages = []
+    for msg in messages:
+        ollama_msg = {"role": msg["role"]}
+        if "content" in msg:
+            ollama_msg["content"] = msg["content"]
+        if "thinking" in msg:
+            ollama_msg["thinking"] = msg["thinking"]
+        if "tool_calls" in msg:
+            # Convert tool_calls to Ollama format
+            tool_calls = []
+            for tc in msg["tool_calls"]:
+                tool_call = {
+                    "function": {
+                        "name": tc["function"]["name"],
+                        "arguments": tc["function"]["arguments"],
+                    }
+                }
+                if "id" in tc:
+                    tool_call["id"] = tc["id"]
+                tool_calls.append(tool_call)
+            ollama_msg["tool_calls"] = tool_calls
+        if "tool_call_id" in msg:
+            ollama_msg["tool_call_id"] = msg["tool_call_id"]
+        ollama_messages.append(ollama_msg)
+
+    payload = {
+        "model": ollama_model,
+        "messages": ollama_messages,
+        "stream": False,
+        "_debug_render_only": True,
+    }
+
+    if tools:
+        payload["tools"] = tools
+
+    try:
+        resp = requests.post(f"{ollama_host}/api/chat", json=payload, timeout=30)
+        resp.raise_for_status()
+        data = resp.json()
+        # Field name is _debug_info with underscore prefix
+        if "_debug_info" in data and "rendered_template" in data["_debug_info"]:
+            return data["_debug_info"]["rendered_template"]
+        return None
+    except requests.exceptions.ConnectionError:
+        print(f"  [ERROR] Cannot connect to Ollama at {ollama_host}", file=sys.stderr)
+        return None
+    except Exception as e:
+        print(f"  [ERROR] Ollama request failed: {e}", file=sys.stderr)
+        return None
+
+
+def compute_diff(hf_prompt: str, ollama_prompt: str) -> str:
+    """Compute a unified diff between HuggingFace and Ollama prompts."""
+    import difflib
+
+    hf_lines = hf_prompt.splitlines(keepends=True)
+    ollama_lines = ollama_prompt.splitlines(keepends=True)
+
+    diff = difflib.unified_diff(
+        ollama_lines,
+        hf_lines,
+        fromfile="Ollama",
+        tofile="HuggingFace",
+        lineterm="",
+    )
+    return "".join(diff)
+
+
+def print_test_output(
+    name: str,
+    messages: list[dict],
+    tools: list[dict] | None,
+    hf_prompt: str,
+    ollama_prompt: str | None = None,
+    as_repr: bool = False,
+):
+    """Print test output in a format suitable for Go test creation and LLM diffing."""
+    print(f"\n{'='*60}")
+    print(f"Test: {name}")
+    print("=" * 60)
+    print("\n--- Input Messages ---")
+    print(json.dumps(messages, indent=2))
+    if tools:
+        print("\n--- Tools ---")
+        print(json.dumps(tools, indent=2))
+
+    if ollama_prompt is not None:
+        # Comparison mode
+        if hf_prompt == ollama_prompt:
+            print("\n--- Result: MATCH ---")
+            print("\n--- Prompt (both identical) ---")
+            if as_repr:
+                print(repr(hf_prompt))
+            else:
+                print(hf_prompt)
+        else:
+            print("\n--- Result: MISMATCH ---")
+            print("\n--- HuggingFace Prompt ---")
+            if as_repr:
+                print(repr(hf_prompt))
+            else:
+                print(hf_prompt)
+            print("\n--- Ollama Prompt ---")
+            if as_repr:
+                print(repr(ollama_prompt))
+            else:
+                print(ollama_prompt)
+            print("\n--- Diff (Ollama -> HuggingFace) ---")
+            diff = compute_diff(hf_prompt, ollama_prompt)
+            if diff:
+                print(diff)
+            else:
+                print("(no line-level diff, check whitespace)")
+    else:
+        # HuggingFace only mode
+        print("\n--- HuggingFace Prompt ---")
+        if as_repr:
+            print(repr(hf_prompt))
+        else:
+            print(hf_prompt)
+
+    print("=" * 60)
+
+
+def run_tests(
+    model: str,
+    as_repr: bool = False,
+    test_filter: str | None = None,
+    ollama_model: str | None = None,
+    ollama_host: str = "http://localhost:11434",
+):
+    """Run all predefined test cases against a model."""
+    if ollama_model:
+        print(f"\nComparing HuggingFace ({model}) vs Ollama ({ollama_model})\n")
+    else:
+        print(f"\nRunning tests against: {model}\n")
+
+    matches = 0
+    mismatches = 0
+    errors = 0
+
+    for test_case in TEST_CASES:
+        name = test_case["name"]
+        messages = test_case["messages"]
+        tools = test_case["tools"]
+
+        # Filter tests if specified
+        if test_filter and test_filter.lower() not in name.lower():
+            continue
+
+        try:
+            hf_prompt = apply_template(model, messages, tools)
+
+            ollama_prompt = None
+            if ollama_model:
+                ollama_prompt = get_ollama_prompt(
+                    ollama_model, messages, tools, ollama_host
+                )
+                if ollama_prompt is None:
+                    errors += 1
+                elif hf_prompt == ollama_prompt:
+                    matches += 1
+                else:
+                    mismatches += 1
+
+            print_test_output(
+                name, messages, tools, hf_prompt, ollama_prompt, as_repr=as_repr
+            )
+        except Exception as e:
+            errors += 1
+            print(f"\n{'='*60}")
+            print(f"Test: {name} - FAILED")
+            print(f"--- Input Messages ---")
+            print(json.dumps(messages, indent=2))
+            if tools:
+                print(f"--- Tools ---")
+                print(json.dumps(tools, indent=2))
+            print(f"--- Error ---")
+            print(f"{e}")
+            print("=" * 60)
+
+    # Print summary if comparing
+    if ollama_model:
+        total = matches + mismatches + errors
+        print(f"\n{'='*60}")
+        print("SUMMARY")
+        print("=" * 60)
+        print(f"  Total:      {total}")
+        print(f"  Matches:    {matches}")
+        print(f"  Mismatches: {mismatches}")
+        print(f"  Errors:     {errors}")
+        print("=" * 60)
+
+
+def show_template(model: str):
+    """Show the chat template for a model."""
+    tokenizer = get_tokenizer(model)
+    print(f"\nChat template for {model}:\n")
+    print("-" * 60)
+    print(tokenizer.chat_template)
+    print("-" * 60)
+
+
+def start_server(host: str = "0.0.0.0", port: int = 8000):
+    """Start the FastAPI server for manual testing."""
+    from typing import Optional, List, Dict, Any as TypingAny
+
+    from fastapi import FastAPI, HTTPException
+    from pydantic import BaseModel
+    import uvicorn
+
+    class Message(BaseModel):
+        role: str
+        content: Optional[str] = None
+        tool_calls: Optional[List[Dict[str, TypingAny]]] = None
+        tool_call_id: Optional[str] = None
+
+    class GeneratePromptRequest(BaseModel):
+        messages: List[Message]
+        model: str = "PrimeIntellect/INTELLECT-3"
+        tools: Optional[List[Dict[str, TypingAny]]] = None
+        inject_tools_as_functions: bool = False
+
+    class GeneratePromptResponse(BaseModel):
+        prompt: str
+        model: str
+
+    app = FastAPI(title="HuggingFace Prompt Generator", version="1.0.0")
+
+    @app.post("/generate-prompt", response_model=GeneratePromptResponse)
+    async def generate_prompt(request: GeneratePromptRequest):
+        try:
+            messages = []
+            for msg in request.messages:
+                message_dict = {"role": msg.role}
+                if msg.content is not None:
+                    message_dict["content"] = msg.content
+                if msg.tool_calls is not None:
+                    tool_calls = []
+                    for tc in msg.tool_calls:
+                        tc_copy = tc.copy()
+                        if "function" in tc_copy and "arguments" in tc_copy["function"]:
+                            args = tc_copy["function"]["arguments"]
+                            if isinstance(args, str):
+                                try:
+                                    tc_copy["function"]["arguments"] = json.loads(args)
+                                except json.JSONDecodeError:
+                                    pass
+                        tool_calls.append(tc_copy)
+                    message_dict["tool_calls"] = tool_calls
+                if msg.tool_call_id is not None:
+                    message_dict["tool_call_id"] = msg.tool_call_id
+                messages.append(message_dict)
+
+            prompt = apply_template(request.model, messages, request.tools)
+            return GeneratePromptResponse(prompt=prompt, model=request.model)
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=str(e))
+
+    @app.get("/health")
+    async def health_check():
+        return {"status": "healthy"}
+
+    print(f"Starting server on http://{host}:{port}")
+    print("Endpoints:")
+    print("  POST /generate-prompt - Generate prompt from messages")
+    print("  GET  /health          - Health check")
+    uvicorn.run(app, host=host, port=port)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="HuggingFace Prompt Testing Tool",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+    parser.add_argument(
+        "--model",
+        "-m",
+        type=str,
+        help="HuggingFace model name (e.g., PrimeIntellect/INTELLECT-3)",
+    )
+    parser.add_argument(
+        "--ollama-model",
+        "-o",
+        type=str,
+        help="Ollama model name to compare against (e.g., qwen3-coder)",
+    )
+    parser.add_argument(
+        "--ollama-host",
+        type=str,
+        default="http://localhost:11434",
+        help="Ollama server URL (default: http://localhost:11434)",
+    )
+    parser.add_argument(
+        "--serve",
+        "-s",
+        action="store_true",
+        help="Start FastAPI server for manual curl testing",
+    )
+    parser.add_argument(
+        "--port",
+        "-p",
+        type=int,
+        default=8000,
+        help="Server port (default: 8000)",
+    )
+    parser.add_argument(
+        "--show-template",
+        "-t",
+        action="store_true",
+        help="Show the chat template for the model",
+    )
+    parser.add_argument(
+        "--repr",
+        "-r",
+        action="store_true",
+        help="Output prompts as Python repr (shows escape sequences)",
+    )
+    parser.add_argument(
+        "--filter",
+        "-f",
+        type=str,
+        help="Filter tests by name (substring match)",
+    )
+
+    args = parser.parse_args()
+
+    if args.serve:
+        start_server(port=args.port)
+    elif args.model:
+        if args.show_template:
+            show_template(args.model)
+        else:
+            run_tests(
+                args.model,
+                as_repr=args.repr,
+                test_filter=args.filter,
+                ollama_model=args.ollama_model,
+                ollama_host=args.ollama_host,
+            )
+    else:
+        parser.print_help()
+        print("\nExample usage:")
+        print("  uv run cmd/chat_template/chat_template.py --model PrimeIntellect/INTELLECT-3")
+        print("  uv run cmd/chat_template/chat_template.py --model Qwen/Qwen3-Coder-480B-A35B-Instruct --ollama-model qwen3-coder")
+        print("  uv run cmd/chat_template/chat_template.py --serve")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()
+
--- a/convert/convert.go
+++ b/convert/convert.go
@@ -206,6 +206,8 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
 		conv = &commandrModel{}
 	case "GptOssForCausalLM":
 		conv = &gptossModel{}
+	case "DeepseekOCRForCausalLM":
+		conv = &deepseekocr{}
 	default:
 		return fmt.Errorf("unsupported architecture %q", p.Architectures[0])
 	}
--- a/convert/convert_deepseekocr.go
+++ b/convert/convert_deepseekocr.go
@@ -0,0 +1,136 @@
+package convert
+
+import (
+	"fmt"
+
+	"github.com/ollama/ollama/fs/ggml"
+)
+
+type deepseekocr struct {
+	ModelParameters
+	LanguageConfig struct {
+		MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
+		HiddenSize            uint32 `json:"hidden_size"`
+		HiddenLayers          uint32 `json:"num_hidden_layers"`
+		IntermediateSize      uint32 `json:"intermediate_size"`
+		NumAttentionHeads     uint32 `json:"num_attention_heads"`
+		NumKeyValueHeads      uint32 `json:"num_key_value_heads"`
+		NumRoutedExperts      uint32 `json:"n_routed_experts"`
+		NumSharedExperts      uint32 `json:"n_shared_experts"`
+		NumExpertsPerToken    uint32 `json:"num_experts_per_tok"`
+		FirstKDenseReplace    uint32 `json:"first_k_dense_replace"`
+	} `json:"language_config"`
+
+	VisionConfig struct {
+		ImageSize uint32 `json:"image_size"`
+		Width     struct {
+			Vision struct {
+				Heads     uint32 `json:"heads"`
+				ImageSize uint32 `json:"image_size"`
+				Layers    uint32 `json:"layers"`
+				PatchSize uint32 `json:"patch_size"`
+				Width     uint32 `json:"width"`
+			} `json:"clip-l-14-224"`
+			Sam struct {
+				GlobalAttentionIndexes []int32 `json:"global_attn_indexes"`
+				Heads                  uint32  `json:"heads"`
+				Layers                 uint32  `json:"layers"`
+				Width                  uint32  `json:"width"`
+			} `json:"sam_vit_b"`
+		}
+	} `json:"vision_config"`
+}
+
+func (m *deepseekocr) KV(t *Tokenizer) ggml.KV {
+	kv := m.ModelParameters.KV(t)
+	kv["general.architecture"] = "deepseekocr"
+	kv["block_count"] = m.LanguageConfig.HiddenLayers
+	kv["context_length"] = m.LanguageConfig.MaxPositionEmbeddings
+	kv["embedding_length"] = m.LanguageConfig.HiddenSize
+	kv["feed_forward_length"] = m.LanguageConfig.IntermediateSize
+	kv["attention.head_count"] = m.LanguageConfig.NumAttentionHeads
+	kv["attention.head_count_kv"] = m.LanguageConfig.NumKeyValueHeads
+	kv["expert_count"] = m.LanguageConfig.NumRoutedExperts
+	kv["expert_used_count"] = m.LanguageConfig.NumExpertsPerToken
+	kv["leading_dense_block_count"] = m.LanguageConfig.FirstKDenseReplace
+
+	kv["vision.block_count"] = m.VisionConfig.Width.Vision.Layers
+	kv["vision.embedding_length"] = m.VisionConfig.Width.Vision.Width
+	kv["vision.head_count"] = m.VisionConfig.Width.Vision.Heads
+	kv["vision.image_size"] = m.VisionConfig.Width.Vision.ImageSize
+	kv["vision.patch_size"] = m.VisionConfig.Width.Vision.PatchSize
+
+	kv["sam.block_count"] = m.VisionConfig.Width.Sam.Layers
+	kv["sam.embedding_length"] = m.VisionConfig.Width.Sam.Width
+	kv["sam.head_count"] = m.VisionConfig.Width.Sam.Heads
+	kv["sam.global_attention_indexes"] = m.VisionConfig.Width.Sam.GlobalAttentionIndexes
+	return kv
+}
+
+func (m *deepseekocr) Tensors(s []Tensor) (out []*ggml.Tensor) {
+	merges := make([]merge, m.LanguageConfig.HiddenLayers*3)
+	for i := range m.LanguageConfig.HiddenLayers {
+		merges[i*3+0] = merge{
+			fmt.Sprintf("blk.%d.mlp.experts.*.gate_proj.weight", i),
+			fmt.Sprintf("blk.%d.ffn_gate_exps.weight", i),
+		}
+		merges[i*3+1] = merge{
+			fmt.Sprintf("blk.%d.mlp.experts.*.up_proj.weight", i),
+			fmt.Sprintf("blk.%d.ffn_up_exps.weight", i),
+		}
+		merges[i*3+2] = merge{
+			fmt.Sprintf("blk.%d.mlp.experts.*.down_proj.weight", i),
+			fmt.Sprintf("blk.%d.ffn_down_exps.weight", i),
+		}
+	}
+
+	out, s = mergeTensors(s, merges...)
+	for _, t := range s {
+		out = append(out, &ggml.Tensor{
+			Name:     t.Name(),
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+	return out
+}
+
+func (m *deepseekocr) Replacements() []string {
+	return []string{
+		"model.embed_tokens", "token_embd",
+		"model.layers", "blk",
+		"input_layernorm", "attn_norm",
+		"self_attn.q_proj", "attn_q",
+		"self_attn.k_proj", "attn_k",
+		"self_attn.v_proj", "attn_v",
+		"self_attn.o_proj", "attn_output",
+		"post_attention_layernorm", "ffn_norm",
+		"mlp.gate_proj", "ffn_gate",
+		"mlp.up_proj", "ffn_up",
+		"mlp.down_proj", "ffn_down",
+		"mlp.gate", "ffn_gate_inp",
+		"mlp.shared_experts.gate_proj", "ffn_gate_shexp",
+		"mlp.shared_experts.up_proj", "ffn_up_shexp",
+		"mlp.shared_experts.down_proj", "ffn_down_shexp",
+		"model.norm", "output_norm",
+		"lm_head", "output",
+
+		"model.vision_model", "v",
+		"embeddings.patch_embedding", "patch_embd",
+		"embeddings.class_embedding", "class_embd",
+		"embeddings.position_embedding", "position_embd",
+		"transformer.layers", "blk",
+
+		"model.projector", "mm",
+		"model.image_newline", "mm.image_newline",
+		//nolint:misspell // this misspelling is upstream. fixing it breaks the model
+		"model.view_seperator", "mm.view_seperator",
+
+		"model.sam_model.patch_embed.proj", "s.patch_embd",
+		"model.sam_model.pos_embed", "s.position_embd",
+		"model.sam_model.blocks", "s.blk",
+		"model.sam_model.neck", "s.neck",
+		"model.sam_model.net_", "s.net_",
+	}
+}
--- a/convert/reader.go
+++ b/convert/reader.go
@@ -44,7 +44,10 @@ func (t tensorBase) Kind() uint32 {
 		t.name == "v.positional_embedding_vlm" ||
 		t.name == "v.tile_position_embd.weight" ||
 		t.name == "v.pre_tile_position_embd.weight" ||
-		t.name == "v.post_tile_position_embd.weight" {
+		t.name == "v.post_tile_position_embd.weight" ||
+		t.name == "s.position_embd" ||
+		strings.HasSuffix(t.name, "rel_pos_h") ||
+		strings.HasSuffix(t.name, "rel_pos_w") {
 		// these tensors are always F32
 		return tensorKindFP32
 	}
--- a/convert/reader_safetensors.go
+++ b/convert/reader_safetensors.go
@@ -96,7 +96,10 @@ type safetensor struct {

 func (st safetensor) Kind() uint32 {
 	kind := st.tensorBase.Kind()
-	if !strings.HasPrefix(st.name, "v.") && st.dtype == "BF16" && kind != tensorKindFP32 {
+	if st.dtype == "BF16" &&
+		!strings.HasPrefix(st.name, "v.") &&
+		!strings.HasPrefix(st.name, "s.") &&
+		kind != tensorKindFP32 {
 		kind = tensorKindBF16
 	}

--- a/discover/runner.go
+++ b/discover/runner.go
@@ -65,6 +65,7 @@ func GPUDevices(ctx context.Context, runners []ml.FilteredRunnerDiscovery) []ml.
 		}

 		slog.Info("discovering available GPUs...")
+		detectIncompatibleLibraries()

 		// Warn if any user-overrides are set which could lead to incorrect GPU discovery
 		overrideWarnings()
@@ -98,6 +99,9 @@ func GPUDevices(ctx context.Context, runners []ml.FilteredRunnerDiscovery) []ml.
 					continue
 				} else if jetpack != "" && filepath.Base(dir) != "cuda_"+jetpack {
 					continue
+				} else if jetpack == "" && strings.Contains(filepath.Base(dir), "cuda_jetpack") {
+					slog.Debug("jetpack not detected (set JETSON_JETPACK or OLLAMA_LLM_LIBRARY to override), skipping", "libDir", dir)
+					continue
 				} else if !envconfig.EnableVulkan() && strings.Contains(filepath.Base(dir), "vulkan") {
 					slog.Info("experimental Vulkan support disabled.  To enable, set OLLAMA_VULKAN=1")
 					continue
@@ -125,10 +129,20 @@ func GPUDevices(ctx context.Context, runners []ml.FilteredRunnerDiscovery) []ml.
 		supportedMu := sync.Mutex{}
 		supported := make(map[string]map[string]map[string]int) // [Library][libDir][ID] = pre-deletion devices index
 		for i := range devices {
+			libDir := devices[i].LibraryPath[len(devices[i].LibraryPath)-1]
 			if !devices[i].NeedsInitValidation() {
+				// No need to validate, add to the supported map
+				supportedMu.Lock()
+				if _, ok := supported[devices[i].Library]; !ok {
+					supported[devices[i].Library] = make(map[string]map[string]int)
+				}
+				if _, ok := supported[devices[i].Library][libDir]; !ok {
+					supported[devices[i].Library][libDir] = make(map[string]int)
+				}
+				supported[devices[i].Library][libDir][devices[i].ID] = i
+				supportedMu.Unlock()
 				continue
 			}
-			libDir := devices[i].LibraryPath[len(devices[i].LibraryPath)-1]
 			slog.Debug("verifying if device is supported", "library", libDir, "description", devices[i].Description, "compute", devices[i].Compute(), "id", devices[i].ID, "pci_id", devices[i].PCIID)
 			wg.Add(1)
 			go func(i int) {
@@ -474,3 +488,16 @@ func overrideWarnings() {
 		slog.Warn("if GPUs are not correctly discovered, unset and try again")
 	}
 }
+
+func detectIncompatibleLibraries() {
+	if runtime.GOOS != "windows" {
+		return
+	}
+	basePath, err := exec.LookPath("ggml-base.dll")
+	if err != nil || basePath == "" {
+		return
+	}
+	if !strings.HasPrefix(basePath, ml.LibOllamaPath) {
+		slog.Warn("potentially incompatible library detected in PATH", "location", basePath)
+	}
+}
--- a/docs/faq.mdx
+++ b/docs/faq.mdx
@@ -57,8 +57,13 @@ ollama ps
 ```

 <Info>
-  **Output**: ``` NAME ID SIZE PROCESSOR UNTIL llama3:70b bcfb190ca3a7 42 GB
-  100% GPU 4 minutes from now ```
+
+**Output**:
+
+```
+NAME        ID            SIZE    PROCESSOR   UNTIL
+llama3:70b  bcfb190ca3a7  42 GB   100% GPU    4 minutes from now
+```
 </Info>

 The `Processor` column will show which memory the model was loaded in to:
@@ -385,4 +390,4 @@ Ollama for Windows and macOS register as a login item during installation.  You
 - In `Task Manager` go to the `Startup apps` tab, search for `ollama` then click `Disable`

 **MacOS**
- Open `Settings` and search for "Login Items", find the `Ollama` entry under "Allow in the Background`, then click the slider to disable.
+- Open `Settings` and search for "Login Items", find the `Ollama` entry under "Allow in the Background`, then click the slider to disable.
--- a/docs/modelfile.mdx
+++ b/docs/modelfile.mdx
@@ -149,9 +149,6 @@ PARAMETER <parameter> <parametervalue>

 | Parameter      | Description                                                                                                                                                                                                                                                                                                                                                                     | Value Type | Example Usage        |
 | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | -------------------- |
-| mirostat       | Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)                                                                                                                                                                                                                                                                 | int        | mirostat 0           |
-| mirostat_eta   | Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1)                                                                                                                                                | float      | mirostat_eta 0.1     |
-| mirostat_tau   | Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0)                                                                                                                                                                                                                                 | float      | mirostat_tau 5.0     |
 | num_ctx        | Sets the size of the context window used to generate the next token. (Default: 2048)                                                                                                                                                                                                                                                                                            | int        | num_ctx 4096         |
 | repeat_last_n  | Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)                                                                                                                                                                                                                                                                   | int        | repeat_last_n 64     |
 | repeat_penalty | Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)                                                                                                                                                                                             | float      | repeat_penalty 1.1   |
--- a/fs/ggml/ggml.go
+++ b/fs/ggml/ggml.go
@@ -249,6 +249,9 @@ func (kv KV) OllamaEngineRequired() bool {
 		"qwen25vl",
 		"qwen3", "qwen3moe",
 		"qwen3vl", "qwen3vlmoe",
+		"deepseekocr",
+		"deepseek2",
+		"nomic-bert",
 	}, kv.Architecture())
 }

--- a/harmony/harmonyparser.go
+++ b/harmony/harmonyparser.go
@@ -388,9 +388,9 @@ func NewFunctionNameMap() *FunctionNameMap {
 	}
 }

-// Init initializes the handler with tools and optional last message
+// Init initializes the handler with tools, optional last message, and think value
 // Implements the Parser interface
-func (h *HarmonyMessageHandler) Init(tools []api.Tool, lastMessage *api.Message) []api.Tool {
+func (h *HarmonyMessageHandler) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
 	// Initialize the harmony parser
 	if h.HarmonyParser == nil {
 		h.HarmonyParser = &HarmonyParser{
--- a/kvcache/causal.go
+++ b/kvcache/causal.go
@@ -3,7 +3,6 @@ package kvcache
 import (
 	"errors"
 	"fmt"
-	"log/slog"
 	"math"
 	"slices"

@@ -40,18 +39,18 @@ type Causal struct {

 	// ** current forward pass **

-	// the active layer for Get and Put
-	curLayer int
-
-	// starting location for data storage for this batch
-	curLoc int
-
 	// size of the current batch
 	curBatchSize int

+	// locations for data storage for this batch
+	curLoc ml.Tensor
+
 	// mask of the cache as used by this batch
 	curMask ml.Tensor

+	// the active layer for Get and Put
+	curLayer int
+
 	// locations in the cache that are needed for this batch
 	curCellRange cellRange

@@ -206,45 +205,47 @@ func (c *Causal) StartForward(ctx ml.Context, batch input.Batch, reserve bool) e
 	c.curPositions = batch.Positions
 	c.opts.Except = nil

+	var locs []int32
 	if !reserve {
 		c.updateSlidingWindow()

 		var err error
-		c.curLoc, err = c.findStartLoc()
-		if errors.Is(err, ErrKvCacheFull) {
-			c.defrag()
-			c.curLoc, err = c.findStartLoc()
-		}
+		locs, err = c.findLocs()
 		if err != nil {
 			return err
 		}

 		for i, pos := range batch.Positions {
 			seq := batch.Sequences[i]
+			loc := int(locs[i])

-			c.cells[c.curLoc+i] = cacheCell{pos: pos, sequences: []int{seq}}
+			c.cells[loc] = cacheCell{pos: pos, sequences: []int{seq}}

 			seqRange, ok := c.cellRanges[seq]
 			if !ok {
 				seqRange = newRange()
 			}

-			seqRange.min = min(seqRange.min, c.curLoc+i)
-			c.curCellRange.min = min(c.curCellRange.min, c.curLoc+i)
+			seqRange.min = min(seqRange.min, loc)
+			c.curCellRange.min = min(c.curCellRange.min, loc)

-			seqRange.max = max(seqRange.max, c.curLoc+i)
-			c.curCellRange.max = max(c.curCellRange.max, c.curLoc+i)
+			seqRange.max = max(seqRange.max, loc)
+			c.curCellRange.max = max(c.curCellRange.max, loc)

 			c.cellRanges[seq] = seqRange
 		}
 	} else {
 		// If we are reserving memory, don't update any of the cache metadata but set the size
 		// to the worst case.
-		c.curLoc = 0
+		locs = make([]int32, c.curBatchSize)
+		for i := range locs {
+			locs[i] = int32(i)
+		}
 		c.curCellRange.min = 0
 		c.curCellRange.max = len(c.cells) - 1
 	}

+	c.curLoc = ctx.Input().FromInts(locs, len(locs))
 	c.curMask = c.buildMask(ctx)

 	return nil
@@ -257,22 +258,20 @@ func newRange() cellRange {
 	}
 }

-// Find the first contiguous block of at least curBatchSize
-func (c *Causal) findStartLoc() (int, error) {
-	var start, count int
+// Returns a slice of locations where each token in the batch should be stored
+func (c *Causal) findLocs() ([]int32, error) {
+	loc := make([]int32, 0, c.curBatchSize)
+
 	for i := range c.cells {
 		if len(c.cells[i].sequences) == 0 {
-			count++
-			if count >= c.curBatchSize {
-				return start, nil
+			loc = append(loc, int32(i))
+			if len(loc) >= c.curBatchSize {
+				return loc, nil
 			}
-		} else {
-			start = i + 1
-			count = 0
 		}
 	}

-	return 0, fmt.Errorf("%w (cache: %v batch: %v)", ErrKvCacheFull, len(c.cells), c.curBatchSize)
+	return nil, fmt.Errorf("%w (cache: %v batch: %v)", ErrKvCacheFull, len(c.cells), c.curBatchSize)
 }

 func (c *Causal) updateSlidingWindow() {
@@ -402,145 +401,6 @@ func (c *Causal) buildMask(ctx ml.Context) ml.Tensor {
 	return maskTensor
 }

-func (c *Causal) moveCells(ctx ml.Context, src, dst, length int) {
-	for i, key := range c.keys {
-		if key == nil {
-			continue
-		}
-
-		kHeadDim := key.Dim(0)
-		numKVHeads := key.Dim(1)
-		rowSize := key.Stride(2)
-
-		kSrcView := key.View(ctx, rowSize*src, kHeadDim*numKVHeads*length)
-		kDstView := key.View(ctx, rowSize*dst, kHeadDim*numKVHeads*length)
-
-		value := c.values[i]
-		var vSrcView, vDstView ml.Tensor
-		if c.config.PermutedV {
-			vHeadDim := value.Dim(1)
-			elemSize := value.Stride(0)
-
-			vSrcView = value.View(ctx, elemSize*src, length, len(c.cells)*elemSize, vHeadDim*numKVHeads)
-			vDstView = value.View(ctx, elemSize*dst, length, len(c.cells)*elemSize, vHeadDim*numKVHeads)
-		} else {
-			vHeadDim := value.Dim(0)
-			rowSize := value.Stride(2)
-
-			vSrcView = value.View(ctx, rowSize*src, vHeadDim*numKVHeads*length)
-			vDstView = value.View(ctx, rowSize*dst, vHeadDim*numKVHeads*length)
-		}
-
-		ctx.Forward(
-			kSrcView.Copy(ctx, kDstView),
-			vSrcView.Copy(ctx, vDstView),
-		)
-	}
-}
-
-func (c *Causal) defrag() {
-	slog.Debug("defragmenting kv cache")
-
-	// Defrag strategy:
-	// - Search for empty holes at the beginning of the cache,
-	//   filling them with active data starting at the end
-	// - If there are contiguous elements that need to be moved,
-	//   combine them into a single operation by holding new moves
-	//   until we see that the next one is non-contiguous
-	// - Fill up the context with the maximum number of operations it
-	//   can hold then compute that and continue with a new context
-	//
-	// We could try to optimize placement by grouping blocks from
-	// the same sequences together but most likely the next forward
-	// pass will disrupt this anyways, so the real world benefit
-	// seems limited as this time.
-
-	ctx := c.backend.NewContext()
-
-	// For every move, 6 tensors are required per layer (2 views and a
-	// copy for each of k and v). We also need to refer to the original
-	// k and v cache tensors - once per layer, not per move.
-	layers := 0
-	for _, key := range c.keys {
-		if key == nil {
-			continue
-		}
-		layers++
-	}
-
-	maxMoves := (ctx.MaxGraphNodes() - 2*layers) / (6 * layers)
-	moves := 0
-
-	var pendingSrc, pendingDst, pendingLen int
-	src := len(c.cells) - 1
-
-	for dst := 0; dst < src; dst++ {
-		if len(c.cells[dst].sequences) == 0 {
-			for ; src > dst; src-- {
-				if len(c.cells[src].sequences) != 0 {
-					c.cells[dst] = c.cells[src]
-					c.cells[src] = cacheCell{}
-
-					if pendingLen > 0 {
-						if src == pendingSrc-pendingLen && dst == pendingDst+pendingLen {
-							pendingSrc = src
-							pendingLen++
-							break
-						} else {
-							c.moveCells(ctx, pendingSrc, pendingDst, pendingLen)
-							moves++
-						}
-					}
-
-					pendingSrc = src
-					pendingDst = dst
-					pendingLen = 1
-
-					break
-				}
-			}
-		}
-
-		if moves >= maxMoves {
-			ctx.Compute()
-			ctx.Close()
-			ctx = c.backend.NewContext()
-
-			moves = 0
-		}
-	}
-
-	if pendingLen > 0 {
-		c.moveCells(ctx, pendingSrc, pendingDst, pendingLen)
-		moves++
-	}
-
-	if moves > 0 {
-		ctx.Compute()
-	}
-	ctx.Close()
-
-	// Reset range metadata
-	for seq := range c.cellRanges {
-		seqRange := newRange()
-
-		for i, cell := range c.cells {
-			if slices.Contains(cell.sequences, seq) {
-				if i < seqRange.min {
-					seqRange.min = i
-				}
-				if i > seqRange.max {
-					seqRange.max = i
-				}
-			}
-		}
-
-		c.cellRanges[seq] = seqRange
-	}
-
-	c.updateSlidingWindow()
-}
-
 func (c *Causal) SetLayer(layer int) {
 	c.curLayer = layer
 }
@@ -625,18 +485,25 @@ func (c *Causal) Put(ctx ml.Context, key, value ml.Tensor) {
 		}
 	}

-	rowSize := c.keys[c.curLayer].Stride(2)
-	ctx.Forward(key.Copy(ctx, c.keys[c.curLayer].View(ctx, rowSize*c.curLoc, kHeadDim*numKVHeads*batchSize)))
+	key = key.Reshape(ctx, kHeadDim*numKVHeads, batchSize)
+	keyCache := c.keys[c.curLayer]
+	keyCache = keyCache.Reshape(ctx, kHeadDim*numKVHeads, len(c.cells))
+	ctx.Forward(keyCache.SetRows(ctx, key, c.curLoc))

 	if c.config.PermutedV {
-		elemSize := c.values[c.curLayer].Stride(0)
+		value = value.Reshape(ctx, vHeadDim*numKVHeads, 1, batchSize)
+		value = value.Permute(ctx, 2, 0, 1, 3)

-		value = value.Permute(ctx, 1, 2, 0, 3)
-		ctx.Forward(value.Copy(ctx, c.values[c.curLayer].View(ctx, elemSize*c.curLoc, batchSize, len(c.cells)*elemSize, vHeadDim*numKVHeads)))
+		valueCache := c.values[c.curLayer]
+		valueCache = valueCache.Reshape(ctx, 1, len(c.cells), vHeadDim*numKVHeads)
+
+		ctx.Forward(valueCache.SetRows(ctx, value, c.curLoc))
 	} else {
-		rowSize := c.values[c.curLayer].Stride(2)
+		value = value.Reshape(ctx, vHeadDim*numKVHeads, batchSize)
+		valueCache := c.values[c.curLayer]
+		valueCache = valueCache.Reshape(ctx, vHeadDim*numKVHeads, len(c.cells))

-		ctx.Forward(value.Copy(ctx, c.values[c.curLayer].View(ctx, rowSize*c.curLoc, vHeadDim*numKVHeads*batchSize)))
+		ctx.Forward(valueCache.SetRows(ctx, value, c.curLoc))
 	}
 }

--- a/kvcache/causal_test.go
+++ b/kvcache/causal_test.go
--- a/llama/patches/0028-Add-memory-detection-using-DXGI-PDH.patch
+++ b/llama/patches/0028-Add-memory-detection-using-DXGI-PDH.patch
@@ -38,7 +38,7 @@ index 44ae76d66..639d551a2 100644
 #ifdef __cplusplus
 }
 diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
-index d2c278a35..221e29509 100644
+index ca02ea079..c12b069e5 100644
 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
 +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -73,6 +73,7 @@ DispatchLoaderDynamic & ggml_vk_default_dispatcher();
--- a/llama/patches/0029-vulkan-Call-ggml_vk_buffer_write_2d-from-ggml_vk_buf.patch
+++ b/llama/patches/0029-vulkan-Call-ggml_vk_buffer_write_2d-from-ggml_vk_buf.patch
@@ -11,7 +11,7 @@ vidmem optimization.
 1 file changed, 1 insertion(+), 4 deletions(-)

 diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
-index 221e29509..18b7cbccf 100644
+index c12b069e5..76c78c2ea 100644
 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
 +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -5654,14 +5654,11 @@ static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& sr
--- a/llama/patches/0030-Vulkan-MMQ-Integer-Dot-Refactor-and-K-Quant-support-.patch
+++ b/llama/patches/0030-Vulkan-MMQ-Integer-Dot-Refactor-and-K-Quant-support-.patch
@@ -50,7 +50,7 @@ Subject: [PATCH] Vulkan MMQ Integer Dot Refactor and K-Quant support (#16536)
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl

 diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
-index 18b7cbccf..53b57c179 100644
+index 76c78c2ea..7669ed206 100644
 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
 +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -488,6 +488,7 @@ struct vk_device_struct {
--- a/llama/patches/0031-vulkan-Update-topk_moe-fusion-to-handle-gpt-s-late-s.patch
+++ b/llama/patches/0031-vulkan-Update-topk_moe-fusion-to-handle-gpt-s-late-s.patch
@@ -58,7 +58,7 @@ index 639d551a2..e5c446d1d 100644
 GGML_API size_t gguf_type_size(enum gguf_type type);
 GGML_API struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_params params);
 diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
-index 53b57c179..b2855b078 100644
+index 7669ed206..63a762ec2 100644
 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
 +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -387,12 +387,76 @@ static constexpr uint32_t num_argsort_pipelines = 11;
--- a/llama/patches/0032-vulkan-Fuse-rope-set_rows-16769.patch
+++ b/llama/patches/0032-vulkan-Fuse-rope-set_rows-16769.patch
@@ -31,7 +31,7 @@ Add new backend tests.
 6 files changed, 371 insertions(+), 117 deletions(-)

 diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
-index b2855b078..aaf4334b5 100644
+index 63a762ec2..db92a7901 100644
 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
 +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -458,6 +458,11 @@ static topk_moe_mode ggml_vk_num_additional_ops_to_topk_moe_mode(uint32_t num) {
--- a/llama/patches/0033-vulkan-Handle-argsort-with-a-large-number-of-rows-16.patch
+++ b/llama/patches/0033-vulkan-Handle-argsort-with-a-large-number-of-rows-16.patch
@@ -9,7 +9,7 @@ Subject: [PATCH] vulkan: Handle argsort with a large number of rows (#16851)
 2 files changed, 16 insertions(+), 4 deletions(-)

 diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
-index aaf4334b5..3604ceb04 100644
+index db92a7901..e959674d1 100644
 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
 +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -1084,6 +1084,7 @@ struct vk_op_soft_max_push_constants {
--- a/llama/patches/0035-vulkan-Fix-crash-when-FP16-mul_mat-accumulation-is-n.patch
+++ b/llama/patches/0035-vulkan-Fix-crash-when-FP16-mul_mat-accumulation-is-n.patch
@@ -20,7 +20,7 @@ Subject: [PATCH] vulkan: Fix crash when FP16 mul_mat accumulation is not
 1 file changed, 13 insertions(+), 7 deletions(-)

 diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
-index 3604ceb04..80185d9f0 100644
+index e959674d1..903050b0b 100644
 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
 +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -146,8 +146,13 @@ static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline);
--- a/llama/patches/0036-ggml-cuda-skip-large-batches.patch
+++ b/llama/patches/0036-ggml-cuda-skip-large-batches.patch
@@ -0,0 +1,25 @@
+From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
+From: Michael Yang <git@mxy.ng>
+Date: Tue, 18 Nov 2025 11:13:04 -0800
+Subject: [PATCH] ggml-cuda: skip large batches
+
+cuda panics on batches larger than 1024 so mark it as unsupported to
+fallback to cpu
+---
+ ggml/src/ggml-cuda/ggml-cuda.cu | 3 +++
+ 1 file changed, 3 insertions(+)
+
+diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
+index f1a20e7fe..1a71e07c9 100644
+--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
+@@ -3677,6 +3677,9 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
+                 if (b->type == GGML_TYPE_F16 && a->type != GGML_TYPE_F16) {
+                     return false;
+                 }
+                if (op->op == GGML_OP_MUL_MAT && b->ne[2] * b->ne[3] > 1024) {
+                    return false;
+                }
+ #ifdef GGML_USE_MUSA
+                 const int cc = ggml_cuda_info().devices[dev_ctx->device].cc;
+                 if (b->ne[2]*b->ne[3] > 1 && !ggml_is_transposed(a) && !ggml_is_transposed(b)) {
--- a/llama/patches/0036-win-exit-instead-of-abort.patch
+++ b/llama/patches/0036-win-exit-instead-of-abort.patch
@@ -0,0 +1,28 @@
+From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
+From: Daniel Hiltgen <daniel@ollama.com>
+Date: Tue, 18 Nov 2025 09:58:23 -0800
+Subject: [PATCH] win: exit instead of abort
+
+---
+ ggml/src/ggml.c | 7 ++++++-
+ 1 file changed, 6 insertions(+), 1 deletion(-)
+
+diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
+index 9be35c1be..923c33d05 100644
+--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
+@@ -229,8 +229,13 @@ void ggml_abort(const char * file, int line, const char * fmt, ...) {
+         fprintf(stderr, "%s\n", message);
+         ggml_print_backtrace();
+     }
+-
+#if defined(_WIN32)
+    fflush(stderr);
+    fflush(stdout);
+    exit(1);
+#else
+     abort();
+#endif
+ }
+ 
+ // ggml_print_backtrace is registered with std::set_terminate by ggml.cpp
--- a/ml/backend.go
+++ b/ml/backend.go
@@ -173,6 +173,7 @@ type Tensor interface {
 	Cos(ctx Context) Tensor
 	Tanh(ctx Context) Tensor
 	GELU(ctx Context, up ...Tensor) Tensor
+	QuickGELU(ctx Context, up ...Tensor) Tensor
 	SILU(ctx Context, up ...Tensor) Tensor
 	RELU(ctx Context, up ...Tensor) Tensor
 	Sigmoid(ctx Context) Tensor
@@ -193,6 +194,7 @@ type Tensor interface {
 	Repeat(ctx Context, dim, n int) Tensor
 	Concat(ctx Context, t2 Tensor, dim int) Tensor
 	Rows(ctx Context, t2 Tensor) Tensor
+	SetRows(ctx Context, src Tensor, idxs Tensor) Tensor
 	Copy(ctx Context, t2 Tensor) Tensor
 	Duplicate(ctx Context) Tensor

@@ -207,6 +209,8 @@ type Tensor interface {
 	Stddev(ctx Context) Tensor
 	Sqr(ctx Context) Tensor
 	Sqrt(ctx Context) Tensor
+
+	Interpolate(ctx Context, dims [4]int, samplingMode SamplingMode) Tensor
 }

 // ScaledDotProductAttention implements a fused attention
@@ -372,3 +376,10 @@ const (
 	DTypeI32
 	DTypeMXFP4
 )
+
+type SamplingMode int
+
+const (
+	SamplingModeNearest SamplingMode = iota
+	SamplingModeBilinear
+)
--- a/ml/backend/ggml/ggml.go
+++ b/ml/backend/ggml/ggml.go
@@ -314,7 +314,7 @@ func New(modelPath string, params ml.BackendParams) (ml.Backend, error) {
 			"altup_proj", "altup_unembd_proj",
 			"per_layer_token_embd", "per_layer_model_proj", "per_layer_proj_norm"):
 			createTensor(tensor{source: t}, output.bts, blocks)
-		case strings.HasPrefix(t.Name, "v.") || strings.HasPrefix(t.Name, "mm."):
+		case strings.HasPrefix(t.Name, "v.") || strings.HasPrefix(t.Name, "mm.") || strings.HasPrefix(t.Name, "s."):
 			// TODO: assign vision tensors to the gpu if possible
 			createTensor(tensor{source: t}, output.bts, blocks)
 		case contains(t.Name, "rope_freqs", "rope_factors_long", "rope_factors_short"):
@@ -1338,6 +1338,13 @@ func (t *Tensor) Rows(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
 	}
 }

+func (t *Tensor) SetRows(ctx ml.Context, src ml.Tensor, idxs ml.Tensor) ml.Tensor {
+	return &Tensor{
+		b: t.b,
+		t: C.ggml_set_rows(ctx.(*Context).ctx, t.t, src.(*Tensor).t, idxs.(*Tensor).t),
+	}
+}
+
 func (t *Tensor) Copy(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
 	return &Tensor{
 		b: t.b,
@@ -1378,6 +1385,10 @@ func inferShape(t *Tensor, shape []int) {
 }

 func (t *Tensor) Reshape(ctx ml.Context, shape ...int) ml.Tensor {
+	if !C.ggml_is_contiguous(t.t) {
+		return t.Contiguous(ctx, shape...)
+	}
+
 	if slices.Contains(shape, -1) {
 		inferShape(t, shape)
 	}
@@ -1567,6 +1578,16 @@ func (t *Tensor) GELU(ctx ml.Context, t2 ...ml.Tensor) ml.Tensor {
 	}
 }

+func (t *Tensor) QuickGELU(ctx ml.Context, t2 ...ml.Tensor) ml.Tensor {
+	var tt *C.struct_ggml_tensor
+	if len(t2) > 0 {
+		tt = C.ggml_geglu_quick_split(ctx.(*Context).ctx, t.t, t2[0].(*Tensor).t)
+	} else {
+		tt = C.ggml_gelu_quick_inplace(ctx.(*Context).ctx, t.t)
+	}
+	return &Tensor{b: t.b, t: tt}
+}
+
 func (t *Tensor) SILU(ctx ml.Context, t2 ...ml.Tensor) ml.Tensor {
 	if len(t2) > 0 {
 		return &Tensor{
@@ -1724,6 +1745,23 @@ func (t *Tensor) Sqrt(ctx ml.Context) ml.Tensor {
 	}
 }

+func (t *Tensor) Interpolate(ctx ml.Context, dims [4]int, samplingMode ml.SamplingMode) ml.Tensor {
+	var mode C.uint32_t
+	switch samplingMode {
+	case ml.SamplingModeNearest:
+		mode = C.GGML_SCALE_MODE_NEAREST
+	case ml.SamplingModeBilinear:
+		mode = C.GGML_SCALE_MODE_BILINEAR
+	default:
+		panic("unsupported interpolate mode")
+	}
+
+	return &Tensor{
+		b: t.b,
+		t: C.ggml_interpolate(ctx.(*Context).ctx, t.t, C.int64_t(dims[0]), C.int64_t(dims[1]), C.int64_t(dims[2]), C.int64_t(dims[3]), mode),
+	}
+}
+
 // Slice returns a view of the tensor sliced along dim from low to high in step steps.
 // Slice panics if the dimension is invalid or the slice parameters are out of range.
 // If dim=0 and step>1, the tensor is a copy rather than a view to ensure proper shape.
--- a/ml/backend/ggml/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ml/backend/ggml/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -3677,6 +3677,9 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                if (b->type == GGML_TYPE_F16 && a->type != GGML_TYPE_F16) {
                    return false;
                }
+                if (op->op == GGML_OP_MUL_MAT && b->ne[2] * b->ne[3] > 1024) {
+                    return false;
+                }
 #ifdef GGML_USE_MUSA
                const int cc = ggml_cuda_info().devices[dev_ctx->device].cc;
                if (b->ne[2]*b->ne[3] > 1 && !ggml_is_transposed(a) && !ggml_is_transposed(b)) {
--- a/ml/backend/ggml/ggml/src/ggml.c
+++ b/ml/backend/ggml/ggml/src/ggml.c
@@ -229,8 +229,13 @@ void ggml_abort(const char * file, int line, const char * fmt, ...) {
        fprintf(stderr, "%s\n", message);
        ggml_print_backtrace();
    }
-
+#if defined(_WIN32)
+    fflush(stderr);
+    fflush(stdout);
+    exit(1);
+#else
    abort();
+#endif
 }

 // ggml_print_backtrace is registered with std::set_terminate by ggml.cpp
--- a/model/imageproc/images.go
+++ b/model/imageproc/images.go
@@ -25,12 +25,15 @@ const (

 // Composite returns an image with the alpha channel removed by drawing over a white background.
 func Composite(img image.Image) image.Image {
-	dst := image.NewRGBA(img.Bounds())
-
 	white := color.RGBA{255, 255, 255, 255}
-	draw.Draw(dst, dst.Bounds(), &image.Uniform{white}, image.Point{}, draw.Src)
-	draw.Draw(dst, dst.Bounds(), img, img.Bounds().Min, draw.Over)
+	return CompositeColor(img, white)
+}

+// CompositeColor returns an image with the alpha channel removed by drawing over a white background.
+func CompositeColor(img image.Image, color color.Color) image.Image {
+	dst := image.NewRGBA(img.Bounds())
+	draw.Draw(dst, dst.Bounds(), &image.Uniform{color}, image.Point{}, draw.Src)
+	draw.Draw(dst, dst.Bounds(), img, img.Bounds().Min, draw.Over)
 	return dst
 }

@@ -55,6 +58,31 @@ func Resize(img image.Image, newSize image.Point, method int) image.Image {
 	return dst
 }

+// Pad returns an image which has been resized to fit within a new size, preserving aspect ratio, and padded with a color.
+func Pad(img image.Image, newSize image.Point, color color.Color, kernel draw.Interpolator) image.Image {
+	dst := image.NewRGBA(image.Rect(0, 0, newSize.X, newSize.Y))
+	draw.Draw(dst, dst.Bounds(), &image.Uniform{color}, image.Point{}, draw.Src)
+
+	var minPoint, maxPoint image.Point
+	if img.Bounds().Dx() > img.Bounds().Dy() {
+		// landscape
+		height := newSize.X * img.Bounds().Dy() / img.Bounds().Dx()
+		minPoint = image.Point{0, (newSize.Y - height) / 2}
+		maxPoint = image.Point{newSize.X, height + minPoint.Y}
+	} else {
+		// portrait
+		width := newSize.Y * img.Bounds().Dx() / img.Bounds().Dy()
+		minPoint = image.Point{(newSize.X - width) / 2, 0}
+		maxPoint = image.Point{minPoint.X + width, newSize.Y}
+	}
+
+	kernel.Scale(dst, image.Rectangle{
+		Min: minPoint,
+		Max: maxPoint,
+	}, img, img.Bounds(), draw.Over, nil)
+	return dst
+}
+
 // Normalize returns a slice of float32 containing each of the r, g, b values for an image normalized around a value.
 func Normalize(img image.Image, mean, std [3]float32, rescale bool, channelFirst bool) []float32 {
 	var pixelVals []float32
--- a/model/models/bert/embed.go
+++ b/model/models/bert/embed.go
@@ -156,6 +156,7 @@ func New(c fs.Config) (model.Model, error) {
 					)),
 				},
 			},
+			true,
 		)
 	default:
 		return nil, model.ErrUnsupportedTokenizer
--- a/model/models/deepseek2/model.go
+++ b/model/models/deepseek2/model.go
@@ -254,6 +254,30 @@ func New(c fs.Config) (model.Model, error) {
 	keyLength := int(cmp.Or(c.Uint("attention.key_length_mla"), c.Uint("attention.key_length")))
 	valueLength := int(cmp.Or(c.Uint("attention.value_length_mla"), c.Uint("attention.value_length")))

+	var pre []string
+	switch c.String("tokenizer.ggml.pre") {
+	case "deepseek-v3":
+		pre = []string{
+			// Split regex into multiple parts (according to DeepSeek3's regex)
+			"\\p{N}{1,3}",
+			`[一-龥぀-ゟ゠-ヿ]+`,
+			"[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\r\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\r\n]*|\\s*[\r\n]+|\\s+(?!\\S)|\\s+",
+		}
+	case "deepseek-llm":
+		// TODO: these models haven't been vetted so skip for now
+		// pre = []string{
+		// 	"[\r\n]",
+		// 	"\\s?[A-Za-zµÀ-ÖØ-öø-ƺƼ-ƿǄ-ʓʕ-ʯͰ-ͳͶͷͻ-ͽͿΆΈ-ΊΌΎ-ΡΣ-ϵϷ-ҁҊ-ԯԱ-ՖႠ-ჅᎠ-Ᏽᏸ-ᏽᲐ-ᲺᲽ-Ჿᴀ-ᴫᵫ-ᵷᵹ-ᶚḀ-ἕἘ-Ἕἠ-ὅὈ-Ὅὐ-ὗὙὛὝὟ-ώᾀ-ᾴᾶ-ᾼιῂ-ῄῆ-ῌῐ-ΐῖ-Ίῠ-Ῥῲ-ῴῶ-ῼℂℇℊ-ℓℕℙ-ℝℤΩℨK-ℭℯ-ℴℹℼ-ℿⅅ-ⅉⅎↃↄⰀ-ⱻⱾ-ⳤⳫ-ⳮⳲⳳꙀ-ꙭꚀ-ꚛꜢ-ꝯꝱ-ꞇꞋ-ꞎꭰ-ꮿﬀ-ﬆﬓ-ﬗＡ-Ｚａ-ｚ𐐀-𐑏𐒰-𐓓𐓘-𐓻𐲀-𐲲𐳀-𐳲𑢠-𑣟𞤀-𞥃]+",
+		// 	"\\s?[!-/:-~！-／：-～‘-‟　-。]+",
+		// 	"\\s+$",
+		// 	"[一-龥ࠀ-一가-퟿]+",
+		// 	"[0-9]",
+		// }
+		fallthrough
+	default:
+		return nil, model.ErrUnsupportedTokenizer
+	}
+
 	m := Model{
 		BytePairEncoding: model.NewBytePairEncoding(
 			&model.Vocabulary{
@@ -268,10 +292,7 @@ func New(c fs.Config) (model.Model, error) {
 					c.Ints("tokenizer.ggml.eos_token_ids")...,
 				),
 			},
-			// Split regex into multiple parts (according to DeepSeek3's regex)
-			"\\p{N}{1,3}",
-			`[一-龥぀-ゟ゠-ヿ]+`,
-			"[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\r\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\r\n]*|\\s*[\r\n]+|\\s+(?!\\S)|\\s+",
+			pre...,
 		),
 		Layers: layers,
 		Options: &Options{
--- a/model/models/deepseekocr/imageprocessor.go
+++ b/model/models/deepseekocr/imageprocessor.go
@@ -0,0 +1,83 @@
+package deepseekocr
+
+import (
+	"bytes"
+	"image"
+	"image/color"
+	"math"
+	"slices"
+
+	"golang.org/x/image/draw"
+
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/model/imageproc"
+)
+
+type ratio struct {
+	x, y int
+}
+
+func ProcessImage(ctx ml.Context, bts []byte) (ml.Tensor, ml.Tensor, []int, error) {
+	img, _, err := image.Decode(bytes.NewReader(bts))
+	if err != nil {
+		return nil, nil, nil, err
+	}
+
+	minNum, maxNum, imageSize, baseSize := 2, 9, 640, 1024
+	var targetRatios []ratio
+	for n := minNum; n <= maxNum; n++ {
+		for i := 1; i <= n; i++ {
+			for j := 1; j <= n; j++ {
+				if i*j <= maxNum && i*j >= minNum && !slices.Contains(targetRatios, ratio{i, j}) {
+					targetRatios = append(targetRatios, ratio{i, j})
+				}
+			}
+		}
+	}
+
+	targetRatio := findBestAspectRatio(targetRatios, img.Bounds().Dx(), img.Bounds().Dy(), imageSize)
+	targetWidth, targetHeight := imageSize*targetRatio.x, imageSize*targetRatio.y
+	blocks := targetRatio.x * targetRatio.y
+
+	mean := imageproc.ImageNetStandardMean
+	std := imageproc.ImageNetStandardSTD
+
+	var patches []float32
+	resized := imageproc.Resize(img, image.Point{X: targetWidth, Y: targetHeight}, imageproc.ResizeBilinear)
+	for i := range blocks {
+		patch := image.NewRGBA(image.Rect(0, 0, imageSize, imageSize))
+		draw.Draw(patch, patch.Bounds(), resized, image.Point{
+			X: i % (targetWidth / imageSize) * imageSize,
+			Y: i / (targetWidth / imageSize) * imageSize,
+		}, draw.Over)
+
+		patches = append(patches, imageproc.Normalize(patch, mean, std, true, true)...)
+	}
+
+	img = imageproc.CompositeColor(img, color.Gray{})
+	img = imageproc.Pad(img, image.Point{X: baseSize, Y: baseSize}, color.Gray{127}, draw.BiLinear)
+
+	return ctx.Input().FromFloats(patches, imageSize, imageSize, 3, blocks),
+		ctx.Input().FromFloats(imageproc.Normalize(img, mean, std, true, true), baseSize, baseSize, 3),
+		[]int{targetRatio.x, targetRatio.y},
+		nil
+}
+
+func findBestAspectRatio(targetRatios []ratio, width, height, imageSize int) ratio {
+	bestDiff := math.MaxFloat64
+	best := ratio{1, 1}
+	realRatio := float64(width) / float64(height)
+	for _, target := range targetRatios {
+		targetRatio := float64(target.x) / float64(target.y)
+		diff := math.Abs(realRatio - targetRatio)
+		if diff < bestDiff {
+			bestDiff = diff
+			best = target
+		} else if diff == bestDiff {
+			if float64(width*height) > 0.5*float64(imageSize*imageSize*best.x*best.y) {
+				best = target
+			}
+		}
+	}
+	return best
+}
--- a/model/models/deepseekocr/model.go
+++ b/model/models/deepseekocr/model.go
@@ -0,0 +1,192 @@
+package deepseekocr
+
+import (
+	"math"
+	"slices"
+
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type Model struct {
+	model.Base
+	model.TextProcessor
+
+	Sam    *samModel    `gguf:"s"`
+	Vision *visionModel `gguf:"v"`
+	Text   *textModel
+
+	ImageNewline ml.Tensor `gguf:"mm.image_newline"`
+	//nolint:misspell // this misspelling is upstream. fixing it breaks the model
+	ViewSeperator ml.Tensor `gguf:"mm.view_seperator"`
+
+	Projector *nn.Linear `gguf:"mm.layers"`
+}
+
+func (m *Model) EncodeMultimodal(ctx ml.Context, bts []byte) ([]input.Multimodal, error) {
+	patches, original, crop, err := ProcessImage(ctx, bts)
+	if err != nil {
+		return nil, err
+	}
+
+	var outputs []ml.Tensor
+	if true { // TODO: local features if sum(patches) != 0
+		samOutputs := m.Sam.Forward(ctx, patches)
+		visionOutputs := m.Vision.Forward(ctx, patches, samOutputs)
+
+		samOutputs = samOutputs.Reshape(ctx, -1, samOutputs.Dim(2), samOutputs.Dim(3)).Permute(ctx, 1, 0, 2, 3)
+		visionOutputs = visionOutputs.Slice(ctx, 1, 1, visionOutputs.Dim(1), 1)
+		localOutputs := visionOutputs.Concat(ctx, samOutputs, 0)
+		localOutputs = m.Projector.Forward(ctx, localOutputs)
+
+		hw := int(math.Sqrt(float64(localOutputs.Dim(1))))
+		localOutputs = localOutputs.Reshape(ctx, -1, hw, crop[0], crop[1])
+		localOutputs = localOutputs.Permute(ctx, 0, 2, 1, 3)
+		localOutputs = localOutputs.Contiguous(ctx, -1, crop[0]*hw, crop[1]*hw)
+		localOutputs = localOutputs.Concat(ctx, m.ImageNewline.Repeat(ctx, 2, localOutputs.Dim(2)), 1)
+		localOutputs = localOutputs.Reshape(ctx, localOutputs.Dim(0), -1)
+
+		outputs = append(outputs, localOutputs)
+	}
+
+	samOutputs := m.Sam.Forward(ctx, original)
+	visionOutputs := m.Vision.Forward(ctx, original, samOutputs)
+
+	samOutputs = samOutputs.Reshape(ctx, -1, samOutputs.Dim(2), samOutputs.Dim(3)).Permute(ctx, 1, 0, 2, 3)
+	visionOutputs = visionOutputs.Slice(ctx, 1, 1, visionOutputs.Dim(1), 1)
+	globalOutputs := visionOutputs.Concat(ctx, samOutputs, 0)
+	globalOutputs = m.Projector.Forward(ctx, globalOutputs)
+
+	hw := int(math.Sqrt(float64(globalOutputs.Dim(1))))
+	globalOutputs = globalOutputs.Reshape(ctx, -1, hw, hw)
+	globalOutputs = globalOutputs.Concat(ctx, m.ImageNewline.Repeat(ctx, 2, globalOutputs.Dim(2)), 1)
+	globalOutputs = globalOutputs.Reshape(ctx, globalOutputs.Dim(0), -1)
+
+	outputs = append(outputs, globalOutputs, m.ViewSeperator)
+	return []input.Multimodal{
+		{Tensor: outputs[0].Stack(ctx, 1, outputs[1:]...)},
+	}, nil
+}
+
+func (m *Model) PostTokenize(inputs []*input.Input) ([]*input.Input, error) {
+	outputs := make([]*input.Input, 0, len(inputs))
+	for i := range inputs {
+		if inputs[i].Multimodal == nil {
+			outputs = append(outputs, inputs[i])
+			continue
+		}
+
+		t := inputs[i].Multimodal[0].Tensor
+		outputs = append(outputs, &input.Input{
+			Token:          128815,
+			Multimodal:     inputs[i].Multimodal,
+			MultimodalHash: inputs[i].MultimodalHash,
+			SameBatch:      t.Dim(1) - 1,
+		})
+
+		outputs = slices.Grow(outputs, t.Dim(1)-1)
+		outputs = append(outputs, slices.Repeat([]*input.Input{{Token: 128815}}, t.Dim(1)-1)...)
+	}
+	return outputs, nil
+}
+
+func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	inputsEmbeds := m.Text.TokenEmbedding.Forward(ctx, batch.Inputs).Duplicate(ctx)
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
+
+	for _, mm := range batch.Multimodal {
+		t := mm.Multimodal[0].Tensor
+		ctx.Forward(t.Copy(ctx, inputsEmbeds.View(ctx, mm.Index*inputsEmbeds.Stride(1), t.Dim(0)*t.Dim(1))))
+	}
+
+	hiddenStates := inputsEmbeds
+	for i, block := range m.Text.Blocks {
+		if m.Cache != nil {
+			m.Cache.SetLayer(i)
+		}
+
+		var outputs ml.Tensor
+		if i == len(m.Text.Blocks)-1 {
+			outputs = batch.Outputs
+		}
+
+		hiddenStates = block.Forward(ctx, hiddenStates, positions, outputs, m.Cache, m.Text.Options)
+	}
+
+	hiddenStates = m.Text.OutputNorm.Forward(ctx, hiddenStates, m.Text.Options.eps)
+	return m.Text.Output.Forward(ctx, hiddenStates), nil
+}
+
+func init() {
+	model.Register("deepseekocr", func(c fs.Config) (model.Model, error) {
+		textBlocks := make([]textBlock, c.Uint("block_count"))
+		leadingDenseBlockCount := int(c.Uint("leading_dense_block_count", 1))
+		for i := range textBlocks {
+			if i >= leadingDenseBlockCount {
+				textBlocks[i].FeedForward = &textMoe{}
+			} else {
+				textBlocks[i].FeedForward = &textMLP{}
+			}
+		}
+
+		m := Model{
+			TextProcessor: model.NewBytePairEncoding(
+				&model.Vocabulary{
+					Values: c.Strings("tokenizer.ggml.tokens"),
+					Types:  c.Ints("tokenizer.ggml.token_type"),
+					Merges: c.Strings("tokenizer.ggml.merges"),
+					AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+					BOS:    []int32{int32(c.Uint("tokenizer.ggml.bos_token_id"))},
+					AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+					EOS: append(
+						[]int32{int32(c.Uint("tokenizer.ggml.eos_token_id"))},
+						c.Ints("tokenizer.ggml.eos_token_ids")...,
+					),
+				},
+				// Split regex into multiple parts (according to DeepSeek3's regex)
+				"\\p{N}{1,3}",
+				`[一-龥぀-ゟ゠-ヿ]+`,
+				"[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\r\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\r\n]*|\\s*[\r\n]+|\\s+(?!\\S)|\\s+",
+			),
+			Text: &textModel{
+				Blocks: textBlocks,
+				Options: textOptions{
+					hiddenSize:     int(c.Uint("embedding_length")),
+					numHeads:       int(c.Uint("attention.head_count")),
+					numKVHeads:     int(c.Uint("attention.head_count_kv")),
+					numExperts:     int(c.Uint("expert_count")),
+					numExpertsUsed: int(c.Uint("expert_used_count")),
+					ropeBase:       c.Float("rope.freq_base", 10_000),
+					ropeScale:      c.Float("rope.scaling.factor", 1.0),
+					eps:            c.Float("attention.layer_norm_rms_epsilon", 1e-6),
+				},
+			},
+			Vision: &visionModel{
+				Blocks: make([]visionBlock, c.Uint("vision.block_count")),
+				Options: visionOptions{
+					hiddenSize: int(c.Uint("vision.embedding_length")),
+					numHeads:   int(c.Uint("vision.head_count")),
+					imageSize:  int(c.Uint("vision.image_size", 224)),
+					patchSize:  int(c.Uint("vision.patch_size", 14)),
+					eps:        c.Float("vision.attention.layer_norm_epsilon", 1e-5),
+				},
+			},
+			Sam: &samModel{
+				Blocks: make([]samBlock, c.Uint("sam.block_count")),
+				Options: samOptions{
+					hiddenSize:            int(c.Uint("sam.embedding_length")),
+					numHeads:              int(c.Uint("sam.head_count")),
+					eps:                   c.Float("sam.attention.layer_norm_epsilon", 1e-6),
+					globalAttentionLayers: c.Ints("sam.global_attention_indexes"),
+				},
+			},
+		}
+
+		m.Cache = kvcache.NewCausalCache(m.Text.Shift)
+		return &m, nil
+	})
+}
--- a/model/models/deepseekocr/model_sam.go
+++ b/model/models/deepseekocr/model_sam.go
@@ -0,0 +1,225 @@
+package deepseekocr
+
+import (
+	"math"
+	"slices"
+
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+)
+
+type samModel struct {
+	PatchEmbedding    *nn.Conv2D `gguf:"patch_embd"`
+	PositionEmbedding ml.Tensor  `gguf:"position_embd"`
+
+	Blocks []samBlock `gguf:"blk"`
+
+	Neck *samNeck   `gguf:"neck"`
+	Net2 *nn.Conv2D `gguf:"net_2"`
+	Net3 *nn.Conv2D `gguf:"net_3"`
+
+	Options samOptions
+}
+
+func (m *samModel) absolutePositionEmbedding(ctx ml.Context, hiddenStates ml.Tensor) ml.Tensor {
+	source := m.PositionEmbedding.Dim(1)
+	target := hiddenStates.Dim(2)
+	if source != target {
+		positionEmbed := m.PositionEmbedding.Permute(ctx, 2, 0, 1, 3)
+		positionEmbed = positionEmbed.Interpolate(ctx, [4]int{target, target, hiddenStates.Dim(0), 1}, ml.SamplingModeBilinear)
+		return positionEmbed.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
+	}
+
+	return m.PositionEmbedding
+}
+
+func (m *samModel) Forward(ctx ml.Context, t ml.Tensor) ml.Tensor {
+	hiddenStates := m.PatchEmbedding.Forward(ctx, t, 16, 16, 0, 0, 1, 1)
+	hiddenStates = hiddenStates.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
+
+	if m.PositionEmbedding != nil {
+		hiddenStates = hiddenStates.Add(ctx, m.absolutePositionEmbedding(ctx, hiddenStates))
+	}
+
+	for i, block := range m.Blocks {
+		var windowSize int
+		if !slices.Contains(m.Options.globalAttentionLayers, int32(i)) {
+			windowSize = 14
+		}
+
+		hiddenStates = block.Forward(ctx, hiddenStates, windowSize, m.Options)
+	}
+
+	hiddenStates = hiddenStates.Permute(ctx, 2, 0, 1, 3).Contiguous(ctx)
+	hiddenStates = m.Neck.Forward(ctx, hiddenStates, m.Options)
+	hiddenStates = m.Net2.Forward(ctx, hiddenStates, 2, 2, 1, 1, 1, 1)
+	hiddenStates = m.Net3.Forward(ctx, hiddenStates, 2, 2, 1, 1, 1, 1)
+	return hiddenStates
+}
+
+type samOptions struct {
+	hiddenSize,
+	numHeads int
+	eps                   float32
+	globalAttentionLayers []int32
+}
+
+func (o samOptions) headDim() int {
+	return o.hiddenSize / o.numHeads
+}
+
+type samBlock struct {
+	Norm1       *nn.LayerNorm `gguf:"norm1"`
+	Attention   *samAttention `gguf:"attn"`
+	Norm2       *nn.LayerNorm `gguf:"norm2"`
+	FeedForward *samMLP       `gguf:"mlp"`
+}
+
+func (m *samBlock) Forward(ctx ml.Context, hiddenStates ml.Tensor, windowSize int, opts samOptions) ml.Tensor {
+	c, w, h := hiddenStates.Dim(0), hiddenStates.Dim(1), hiddenStates.Dim(2)
+
+	residual := hiddenStates
+	hiddenStates = m.Norm1.Forward(ctx, hiddenStates, opts.eps)
+
+	var pw, ph int
+	if windowSize > 0 {
+		pw = (windowSize - hiddenStates.Dim(1)%windowSize) % windowSize
+		ph = (windowSize - hiddenStates.Dim(2)%windowSize) % windowSize
+		if pw > 0 || ph > 0 {
+			hiddenStates = hiddenStates.Pad(ctx, 0, pw, ph, 0)
+		}
+
+		hiddenStates = hiddenStates.Reshape(ctx, c*windowSize, (w+pw)/windowSize, windowSize, -1)
+		hiddenStates = hiddenStates.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx, c, windowSize, windowSize, -1)
+	}
+
+	hiddenStates = m.Attention.Forward(ctx, hiddenStates, opts)
+
+	if windowSize > 0 {
+		hiddenStates = hiddenStates.Reshape(ctx, c*windowSize, windowSize, (w+pw)/windowSize, -1)
+		hiddenStates = hiddenStates.Permute(ctx, 0, 2, 1, 3)
+		hiddenStates = hiddenStates.Contiguous(ctx, c, w+pw, h+ph, -1)
+		hiddenStates = hiddenStates.Pad(ctx, 0, -pw, -ph, 0)
+	}
+
+	hiddenStates = hiddenStates.Add(ctx, residual)
+
+	residual = hiddenStates
+	hiddenStates = m.Norm2.Forward(ctx, hiddenStates, opts.eps)
+	hiddenStates = m.FeedForward.Forward(ctx, hiddenStates, opts)
+	return hiddenStates.Add(ctx, residual)
+}
+
+type samAttention struct {
+	QKV    *nn.Linear `gguf:"qkv"`
+	Output *nn.Linear `gguf:"proj"`
+
+	RelativePosition *struct {
+		Height ml.Tensor `gguf:"h"`
+		Width  ml.Tensor `gguf:"w"`
+	} `gguf:",pre:rel_pos_"`
+}
+
+func relativeCoordinates(ctx ml.Context, qn, kn int) ml.Tensor {
+	s := make([]int32, qn*kn)
+	for i := range qn {
+		for j := range kn {
+			q := i * max(kn/qn, 1)
+			k := j * max(qn/kn, 1)
+			s[i*kn+j] = int32(q - k + (kn-1)*max(qn/kn, 1))
+		}
+	}
+	return ctx.Input().FromInts(s, qn*kn)
+}
+
+func relativePositions(ctx ml.Context, positions ml.Tensor, qn, kn int) ml.Tensor {
+	maxRelativeDistance := 2*max(qn, kn) - 1
+	if positions.Dim(1) != maxRelativeDistance {
+		// linear interpolation kernel not available so approx. with bilinear interpolation
+		positions = positions.Interpolate(ctx, [4]int{positions.Dim(0), maxRelativeDistance, 1, 1}, ml.SamplingModeBilinear)
+	}
+
+	rc := relativeCoordinates(ctx, qn, kn)
+	return positions.Rows(ctx, rc).Reshape(ctx, positions.Dim(0), kn, qn)
+}
+
+func (m *samAttention) decomposedRelativePositions(ctx ml.Context, query ml.Tensor, qn, kn []int) (ml.Tensor, ml.Tensor) {
+	qh, qw := qn[0], qn[1]
+	kh, kw := kn[0], kn[1]
+
+	rh := relativePositions(ctx, m.RelativePosition.Height, qh, kh)
+	rw := relativePositions(ctx, m.RelativePosition.Width, qw, kw)
+
+	query = query.Contiguous(ctx, query.Dim(0), qw, qh, -1)
+	rh = rh.Mulmat(ctx, query).Reshape(ctx, 1, kh, qh*qw, -1)
+	rw = rw.Mulmat(ctx, query.Permute(ctx, 0, 2, 1, 3)).Permute(ctx, 0, 2, 1, 3).Contiguous(ctx, kw, 1, qh*qw, -1)
+	return rh, rw
+}
+
+func (m *samAttention) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts samOptions) ml.Tensor {
+	w, h, b := hiddenStates.Dim(1), hiddenStates.Dim(2), hiddenStates.Dim(3)
+
+	qkv := m.QKV.Forward(ctx, hiddenStates)
+	qkv = qkv.Reshape(ctx, opts.headDim(), -1, w*h, b)
+	chunks := qkv.Chunk(ctx, 1, opts.numHeads)
+	query, key, value := chunks[0], chunks[1], chunks[2]
+
+	ctx.Forward(query, key, value)
+
+	query = query.Permute(ctx, 0, 2, 1, 3)
+	rh, rw := m.decomposedRelativePositions(ctx, query, []int{h, w}, []int{h, w})
+	mask := rh.Repeat(ctx, 0, rw.Dim(0)).Add(ctx, rw)
+	mask = mask.Reshape(ctx, h*w, -1, opts.numHeads, b)
+
+	key = key.Permute(ctx, 0, 2, 1, 3)
+	scores := key.MulmatFullPrec(ctx, query)
+	scores = scores.Scale(ctx, 1/math.Sqrt(float64(opts.headDim())))
+
+	scores = scores.Add(ctx, mask)
+	scores = scores.Softmax(ctx)
+
+	value = value.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
+	attention := value.Mulmat(ctx, scores)
+	attention = attention.Permute(ctx, 0, 2, 1, 3)
+	attention = attention.Contiguous(ctx, -1, w, h, b)
+	return m.Output.Forward(ctx, attention)
+}
+
+type samMLP struct {
+	Lin1 *nn.Linear `gguf:"lin1"`
+	Lin2 *nn.Linear `gguf:"lin2"`
+}
+
+func (m *samMLP) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts samOptions) ml.Tensor {
+	return m.Lin2.Forward(ctx, m.Lin1.Forward(ctx, hiddenStates).GELU(ctx))
+}
+
+type LayerNorm2D struct {
+	Weight ml.Tensor `gguf:"weight"`
+	Bias   ml.Tensor `gguf:"bias"`
+}
+
+func (ln *LayerNorm2D) Forward(ctx ml.Context, x ml.Tensor, eps float32) ml.Tensor {
+	x = x.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
+	u := x.Mean(ctx)
+	d := x.Sub(ctx, u)
+	s := d.Sqr(ctx).Mean(ctx)
+	x = d.Div(ctx, s.Add(ctx, ctx.Input().FromFloats([]float32{eps}, 1)).Sqrt(ctx))
+	x = x.Mul(ctx, ln.Weight).Add(ctx, ln.Bias)
+	return x.Permute(ctx, 2, 0, 1, 3).Contiguous(ctx)
+}
+
+type samNeck struct {
+	C1  *nn.Conv2D   `gguf:"0"`
+	LN1 *LayerNorm2D `gguf:"1"`
+	C2  *nn.Conv2D   `gguf:"2"`
+	LN2 *LayerNorm2D `gguf:"3"`
+}
+
+func (m *samNeck) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts samOptions) ml.Tensor {
+	hiddenStates = m.C1.Forward(ctx, hiddenStates, 1, 1, 0, 0, 1, 1)
+	hiddenStates = m.LN1.Forward(ctx, hiddenStates, opts.eps)
+	hiddenStates = m.C2.Forward(ctx, hiddenStates, 1, 1, 1, 1, 1, 1)
+	hiddenStates = m.LN2.Forward(ctx, hiddenStates, opts.eps)
+	return hiddenStates
+}
--- a/model/models/deepseekocr/model_text.go
+++ b/model/models/deepseekocr/model_text.go
@@ -0,0 +1,140 @@
+package deepseekocr
+
+import (
+	"math"
+
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/ml/nn/fast"
+	"github.com/ollama/ollama/ml/nn/rope"
+)
+
+type textModel struct {
+	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
+	Blocks         []textBlock   `gguf:"blk"`
+	OutputNorm     *nn.RMSNorm   `gguf:"output_norm"`
+	Output         *nn.Linear    `gguf:"output"`
+
+	Options textOptions
+}
+
+func (m *textModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	return m.Options.applyRotaryPositionalEmbedding(ctx, key, shift), nil
+}
+
+type textOptions struct {
+	hiddenSize,
+	numHeads,
+	numKVHeads,
+	numExperts,
+	numExpertsUsed int
+	ropeBase,
+	ropeScale,
+	eps float32
+}
+
+func (o textOptions) headDim() int {
+	return o.hiddenSize / o.numHeads
+}
+
+func (o textOptions) applyRotaryPositionalEmbedding(ctx ml.Context, t, p ml.Tensor) ml.Tensor {
+	return fast.RoPE(ctx, t, p, o.headDim(), o.ropeBase, 1/o.ropeScale, rope.WithTypeNeoX())
+}
+
+type textBlock struct {
+	AttentionNorm *nn.RMSNorm `gguf:"attn_norm"`
+	Attention     *textAttention
+	MLPNNorm      *nn.RMSNorm `gguf:"ffn_norm"`
+	FeedForward   textFeedForward
+}
+
+func (m *textBlock) Forward(ctx ml.Context, hiddenStates, positions, outputs ml.Tensor, cache kvcache.Cache, opts textOptions) ml.Tensor {
+	residual := hiddenStates
+	hiddenStates = m.AttentionNorm.Forward(ctx, hiddenStates, opts.eps)
+	hiddenStates = m.Attention.Forward(ctx, hiddenStates, positions, cache, opts)
+	if outputs != nil {
+		hiddenStates = hiddenStates.Rows(ctx, outputs)
+		residual = residual.Rows(ctx, outputs)
+	}
+
+	hiddenStates = hiddenStates.Add(ctx, residual)
+
+	residual = hiddenStates
+	hiddenStates = m.MLPNNorm.Forward(ctx, hiddenStates, opts.eps)
+	hiddenStates = m.FeedForward.Forward(ctx, hiddenStates, opts)
+	return hiddenStates.Add(ctx, residual)
+}
+
+type textAttention struct {
+	Query  *nn.Linear `gguf:"attn_q"`
+	Key    *nn.Linear `gguf:"attn_k"`
+	Value  *nn.Linear `gguf:"attn_v"`
+	Output *nn.Linear `gguf:"attn_output"`
+}
+
+func (m *textAttention) Forward(ctx ml.Context, hiddenStates, positions ml.Tensor, cache kvcache.Cache, opts textOptions) ml.Tensor {
+	query := m.Query.Forward(ctx, hiddenStates)
+	query = query.Reshape(ctx, opts.headDim(), opts.numHeads, -1)
+
+	key := m.Key.Forward(ctx, hiddenStates)
+	key = key.Reshape(ctx, opts.headDim(), opts.numKVHeads, -1)
+
+	value := m.Value.Forward(ctx, hiddenStates)
+	value = value.Reshape(ctx, opts.headDim(), opts.numKVHeads, -1)
+
+	query = opts.applyRotaryPositionalEmbedding(ctx, query, positions)
+	key = opts.applyRotaryPositionalEmbedding(ctx, key, positions)
+
+	attention := nn.Attention(ctx, query, key, value, 1./math.Sqrt(float64(opts.headDim())), cache)
+	attention = attention.Reshape(ctx, -1, attention.Dim(2))
+	return m.Output.Forward(ctx, attention)
+}
+
+type textFeedForward interface {
+	Forward(ml.Context, ml.Tensor, textOptions) ml.Tensor
+}
+
+type textMoe struct {
+	Router        *nn.Linear      `gguf:"ffn_gate_inp"`
+	Gate          *nn.LinearBatch `gguf:"ffn_gate_exps"`
+	Up            *nn.LinearBatch `gguf:"ffn_up_exps"`
+	Down          *nn.LinearBatch `gguf:"ffn_down_exps"`
+	SharedExperts *textMLP        `gguf:",suf:_shexp"`
+}
+
+func (m *textMoe) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts textOptions) ml.Tensor {
+	scores := m.Router.Forward(ctx, hiddenStates).Softmax(ctx)
+	indices := scores.TopK(ctx, opts.numExpertsUsed)
+	weights := scores.Reshape(ctx, 1, opts.numExperts, hiddenStates.Dim(1)).Rows(ctx, indices)
+
+	experts := hiddenStates.Reshape(ctx, hiddenStates.Dim(0), 1, hiddenStates.Dim(1))
+	experts = m.Gate.Forward(ctx, experts, indices).SILU(ctx, m.Up.Forward(ctx, experts, indices))
+	experts = m.Down.Forward(ctx, experts, indices)
+	experts = experts.Mul(ctx, weights)
+
+	expert := func(i int) ml.Tensor {
+		return experts.View(
+			ctx, i*experts.Stride(1), experts.Dim(0), experts.Stride(2), experts.Dim(2),
+		)
+	}
+
+	routedStates := expert(0)
+	for i := 1; i < opts.numExpertsUsed; i++ {
+		routedStates = routedStates.Add(ctx, expert(i))
+	}
+
+	sharedStates := m.SharedExperts.Forward(ctx, hiddenStates, opts)
+	return routedStates.Add(ctx, sharedStates)
+}
+
+type textMLP struct {
+	Gate *nn.Linear `gguf:"ffn_gate"`
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+}
+
+func (m *textMLP) Forward(ctx ml.Context, hiddenStates ml.Tensor, _ textOptions) ml.Tensor {
+	hiddenStates = m.Gate.Forward(ctx, hiddenStates).SILU(ctx, m.Up.Forward(ctx, hiddenStates))
+	return m.Down.Forward(ctx, hiddenStates)
+}
--- a/model/models/deepseekocr/model_vision.go
+++ b/model/models/deepseekocr/model_vision.go
@@ -0,0 +1,117 @@
+package deepseekocr
+
+import (
+	"math"
+
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+)
+
+type visionModel struct {
+	PatchEmbedding    *nn.Conv2D    `gguf:"patch_embd"`
+	ClassEmbedding    ml.Tensor     `gguf:"class_embd"`
+	PositionEmbedding *nn.Embedding `gguf:"position_embd"`
+
+	PreLayerNorm *nn.LayerNorm `gguf:"pre_layrnorm"`
+	Blocks       []visionBlock `gguf:"blk"`
+
+	Options visionOptions
+}
+
+func (m *visionModel) absolutePositionEmbedding(ctx ml.Context, embeds ml.Tensor) ml.Tensor {
+	numPatches := m.Options.imageSize / m.Options.patchSize * m.Options.imageSize / m.Options.patchSize
+	positions := ctx.Arange(0, float32(numPatches+1), 1, ml.DTypeI32)
+	positionEmbeds := m.PositionEmbedding.Forward(ctx, positions)
+
+	source := int(math.Sqrt(float64(positionEmbeds.Dim(1) - 1)))
+	target := int(math.Sqrt(float64(embeds.Dim(1) - 1)))
+	if source != target {
+		newPositionEmbeds := positionEmbeds.Slice(ctx, 1, 1, positionEmbeds.Dim(1), 1)
+		newPositionEmbeds = newPositionEmbeds.Reshape(ctx, -1, source, source)
+		newPositionEmbeds = newPositionEmbeds.Permute(ctx, 2, 0, 1, 3).Contiguous(ctx)
+		newPositionEmbeds = newPositionEmbeds.Interpolate(ctx, [4]int{target, target, embeds.Dim(0), 1}, ml.SamplingModeBilinear)
+		newPositionEmbeds = newPositionEmbeds.Permute(ctx, 1, 2, 0, 3)
+		newPositionEmbeds = newPositionEmbeds.Contiguous(ctx, -1, target*target)
+
+		positionEmbeds = positionEmbeds.Slice(ctx, 1, 0, 1, 1).Concat(ctx, newPositionEmbeds, 1)
+	}
+
+	return positionEmbeds
+}
+
+func (m *visionModel) Forward(ctx ml.Context, pixelValues, patchEmbeds ml.Tensor) ml.Tensor {
+	if patchEmbeds == nil {
+		patchEmbeds = m.PatchEmbedding.Forward(ctx, pixelValues, m.Options.patchSize, m.Options.patchSize, 0, 0, 1, 1)
+	}
+
+	patchEmbeds = patchEmbeds.Reshape(ctx, -1, patchEmbeds.Dim(2), patchEmbeds.Dim(3))
+	patchEmbeds = patchEmbeds.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx)
+
+	classEmbeds := m.ClassEmbedding.Repeat(ctx, 2, patchEmbeds.Dim(2))
+	embeds := classEmbeds.Concat(ctx, patchEmbeds, 1)
+	embeds = embeds.Add(ctx, m.absolutePositionEmbedding(ctx, embeds))
+
+	hiddenStates := m.PreLayerNorm.Forward(ctx, embeds, m.Options.eps)
+	for _, block := range m.Blocks {
+		hiddenStates = block.Forward(ctx, hiddenStates, m.Options)
+	}
+
+	return hiddenStates
+}
+
+type visionOptions struct {
+	hiddenSize,
+	numHeads int
+	eps float32
+
+	imageSize, patchSize int
+}
+
+func (o visionOptions) headDim() int {
+	return o.hiddenSize / o.numHeads
+}
+
+type visionBlock struct {
+	Norm1       *nn.LayerNorm    `gguf:"layer_norm1"`
+	Attention   *visionAttention `gguf:"self_attn"`
+	Norm2       *nn.LayerNorm    `gguf:"layer_norm2"`
+	FeedForward *visionMLP       `gguf:"mlp"`
+}
+
+func (m *visionBlock) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts visionOptions) ml.Tensor {
+	residual := hiddenStates
+	hiddenStates = m.Norm1.Forward(ctx, hiddenStates, opts.eps)
+	hiddenStates = m.Attention.Forward(ctx, hiddenStates, opts)
+	hiddenStates = hiddenStates.Add(ctx, residual)
+
+	residual = hiddenStates
+	hiddenStates = m.Norm2.Forward(ctx, hiddenStates, opts.eps)
+	hiddenStates = m.FeedForward.Forward(ctx, hiddenStates)
+	hiddenStates = hiddenStates.Add(ctx, residual)
+	return hiddenStates
+}
+
+type visionAttention struct {
+	QKV    *nn.Linear `gguf:"qkv_proj"`
+	Output *nn.Linear `gguf:"out_proj"`
+}
+
+func (m *visionAttention) Forward(ctx ml.Context, t ml.Tensor, opts visionOptions) ml.Tensor {
+	qkv := m.QKV.Forward(ctx, t)
+	qkv = qkv.Reshape(ctx, opts.headDim(), -1, qkv.Dim(1), qkv.Dim(2))
+	chunks := qkv.Chunk(ctx, 1, opts.numHeads)
+	query, key, value := chunks[0], chunks[1], chunks[2]
+
+	attention := nn.Attention(ctx, query, key, value, 1/math.Sqrt(float64(opts.headDim())), nil)
+	attention = attention.Reshape(ctx, -1, attention.Dim(2), attention.Dim(3))
+	return m.Output.Forward(ctx, attention)
+}
+
+type visionMLP struct {
+	FC1 *nn.Linear `gguf:"fc1"`
+	FC2 *nn.Linear `gguf:"fc2"`
+}
+
+func (m *visionMLP) Forward(ctx ml.Context, t ml.Tensor) ml.Tensor {
+	return m.FC2.Forward(ctx, m.FC1.Forward(ctx, t).QuickGELU(ctx))
+}
--- a/model/models/models.go
+++ b/model/models/models.go
@@ -3,6 +3,7 @@ package models
 import (
 	_ "github.com/ollama/ollama/model/models/bert"
 	_ "github.com/ollama/ollama/model/models/deepseek2"
+	_ "github.com/ollama/ollama/model/models/deepseekocr"
 	_ "github.com/ollama/ollama/model/models/gemma2"
 	_ "github.com/ollama/ollama/model/models/gemma3"
 	_ "github.com/ollama/ollama/model/models/gemma3n"
@@ -11,6 +12,7 @@ import (
 	_ "github.com/ollama/ollama/model/models/llama4"
 	_ "github.com/ollama/ollama/model/models/mistral3"
 	_ "github.com/ollama/ollama/model/models/mllama"
+	_ "github.com/ollama/ollama/model/models/nomicbert"
 	_ "github.com/ollama/ollama/model/models/qwen2"
 	_ "github.com/ollama/ollama/model/models/qwen25vl"
 	_ "github.com/ollama/ollama/model/models/qwen3"
--- a/model/models/nomicbert/model.go
+++ b/model/models/nomicbert/model.go
@@ -0,0 +1,170 @@
+package nomicbert
+
+import (
+	"cmp"
+	"math"
+
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/ml/nn/fast"
+	"github.com/ollama/ollama/ml/nn/pooling"
+	"github.com/ollama/ollama/ml/nn/rope"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type Model struct {
+	model.Base
+	model.TextProcessor
+
+	TokenEmbedding     *nn.Embedding `gguf:"token_embd"`
+	TypeEmbedding      *nn.Embedding `gguf:"token_types"`
+	TokenEmbeddingNorm *nn.LayerNorm `gguf:"token_embd_norm"`
+
+	Layers []EncoderLayer `gguf:"blk"`
+
+	Options
+}
+
+type Options struct {
+	hiddenSize   int
+	numHeads     int
+	headDim      int
+	eps          float32
+	poolingType  pooling.Type
+	normalize    bool
+	ropeFreqBase float32
+}
+
+// Single Encoder Layer
+type EncoderLayer struct {
+	*Attention
+
+	AttentionNorm *nn.LayerNorm `gguf:"attn_output_norm"`
+
+	*MLP
+
+	MLPNorm *nn.LayerNorm `gguf:"layer_output_norm"`
+}
+
+type Attention struct {
+	QKV    *nn.Linear `gguf:"attn_qkv"`
+	Output *nn.Linear `gguf:"attn_output"`
+}
+
+type MLP struct {
+	Gate *nn.Linear `gguf:"ffn_gate"`
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+}
+
+func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	hiddenStates := m.TokenEmbedding.Forward(ctx, batch.Inputs)
+
+	typeEmbed := m.TypeEmbedding.Weight.Slice(ctx, 1, 0, 1, 1)
+	hiddenStates = hiddenStates.Add(ctx, typeEmbed)
+
+	hiddenStates = m.TokenEmbeddingNorm.Forward(ctx, hiddenStates, m.eps)
+
+	positions := ctx.Input().FromInts(batch.Positions, len(batch.Positions))
+
+	for _, layer := range m.Layers {
+		hiddenStates = layer.Forward(ctx, hiddenStates, positions, &m.Options)
+	}
+
+	hiddenStates = m.poolingType.Forward(ctx, hiddenStates)
+
+	if m.normalize {
+		hiddenStates = hiddenStates.L2Norm(ctx, 1e-12)
+	}
+
+	return hiddenStates, nil
+}
+
+func (e *EncoderLayer) Forward(ctx ml.Context, hiddenStates ml.Tensor, positions ml.Tensor, opts *Options) ml.Tensor {
+	residual := hiddenStates
+	hiddenStates = e.Attention.Forward(ctx, hiddenStates, positions, opts)
+	hiddenStates = hiddenStates.Add(ctx, residual)
+	hiddenStates = e.AttentionNorm.Forward(ctx, hiddenStates, opts.eps)
+
+	residual = hiddenStates
+	hiddenStates = e.MLP.Forward(ctx, hiddenStates)
+	hiddenStates = hiddenStates.Add(ctx, residual)
+	hiddenStates = e.MLPNorm.Forward(ctx, hiddenStates, opts.eps)
+
+	return hiddenStates
+}
+
+func (a *Attention) Forward(ctx ml.Context, hiddenStates ml.Tensor, positions ml.Tensor, opts *Options) ml.Tensor {
+	batchSize := hiddenStates.Dim(1)
+
+	qkv := a.QKV.Forward(ctx, hiddenStates)
+
+	qkv = qkv.Reshape(ctx, opts.headDim, opts.numHeads*3, batchSize)
+	chunks := qkv.Chunk(ctx, 1, opts.numHeads)
+	query, key, value := chunks[0], chunks[1], chunks[2]
+
+	query = fast.RoPE(ctx, query, positions, opts.headDim, opts.ropeFreqBase, 1.0, rope.WithTypeNeoX())
+	key = fast.RoPE(ctx, key, positions, opts.headDim, opts.ropeFreqBase, 1.0, rope.WithTypeNeoX())
+
+	attention := nn.Attention(ctx, query, key, value, 1.0/math.Sqrt(float64(opts.headDim)), nil)
+
+	attention = attention.Reshape(ctx, opts.hiddenSize, batchSize)
+
+	return a.Output.Forward(ctx, attention)
+}
+
+func (m *MLP) Forward(ctx ml.Context, hiddenStates ml.Tensor) ml.Tensor {
+	hidden := m.Gate.Forward(ctx, hiddenStates).SILU(ctx, m.Up.Forward(ctx, hiddenStates))
+
+	return m.Down.Forward(ctx, hidden)
+}
+
+func New(c fs.Config) (model.Model, error) {
+	hiddenSize := int(c.Uint("embedding_length"))
+	numHeads := int(c.Uint("attention.head_count"))
+	headDim := hiddenSize / numHeads
+
+	processor := model.NewWordPiece(
+		&model.Vocabulary{
+			Values: c.Strings("tokenizer.ggml.tokens"),
+			Scores: c.Floats("tokenizer.ggml.scores"),
+			Types:  c.Ints("tokenizer.ggml.token_type"),
+			AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+			BOS: []int32{
+				int32(cmp.Or(
+					c.Uint("tokenizer.ggml.cls_token_id"),
+					c.Uint("tokenizer.ggml.bos_token_id"),
+				)),
+			},
+			AddEOS: c.Bool("tokenizer.ggml.add_eos_token", true),
+			EOS: []int32{
+				int32(cmp.Or(
+					c.Uint("tokenizer.ggml.separator_token_id"),
+					c.Uint("tokenizer.ggml.eos_token_id"),
+				)),
+			},
+		},
+		false,
+	)
+
+	return &Model{
+		TextProcessor: processor,
+		Layers:        make([]EncoderLayer, c.Uint("block_count")),
+		Options: Options{
+			hiddenSize:   hiddenSize,
+			numHeads:     numHeads,
+			headDim:      headDim,
+			eps:          c.Float("attention.layer_norm_epsilon"),
+			poolingType:  pooling.Type(c.Uint("pooling_type")),
+			normalize:    c.Bool("normalize_embeddings", false),
+			ropeFreqBase: c.Float("rope.freq_base", 1000.0),
+		},
+	}, nil
+}
+
+func init() {
+	model.Register("nomic-bert", New)
+	model.Register("nomic-bert_embed", New)
+}
--- a/model/parsers/cogito.go
+++ b/model/parsers/cogito.go
@@ -0,0 +1,319 @@
+package parsers
+
+import (
+	"encoding/json"
+	"errors"
+	"log/slog"
+	"strings"
+	"unicode"
+
+	"github.com/ollama/ollama/api"
+)
+
+type CogitoParserState int
+
+const (
+	CogitoCollectingThinking CogitoParserState = iota
+	CogitoCollectingContent
+	CogitoCollectingToolCalls
+	CogitoCollectingToolOutput
+)
+
+const (
+	cogitoThinkingCloseTag    = "</think>"
+	cogitoToolCallsBeginTag   = "<｜tool▁calls▁begin｜>"
+	cogitoToolCallsEndTag     = "<｜tool▁calls▁end｜>"
+	cogitoToolCallBeginTag    = "<｜tool▁call▁begin｜>"
+	cogitoToolCallEndTag      = "<｜tool▁call▁end｜>"
+	cogitoToolSepTag          = "<｜tool▁sep｜>"
+	cogitoToolOutputBeginTag  = "<｜tool▁output▁begin｜>"
+	cogitoToolOutputEndTag    = "<｜tool▁output▁end｜>"
+	cogitoToolOutputsBeginTag = "<｜tool▁outputs▁begin｜>"
+	cogitoToolOutputsEndTag   = "<｜tool▁outputs▁end｜>"
+)
+
+type CogitoParser struct {
+	state  CogitoParserState
+	buffer strings.Builder
+}
+
+func (p *CogitoParser) HasToolSupport() bool {
+	return true
+}
+
+func (p *CogitoParser) HasThinkingSupport() bool {
+	return true
+}
+
+func (p *CogitoParser) setInitialState(lastMessage *api.Message, tools []api.Tool, thinkValue *api.ThinkValue) {
+	prefill := lastMessage != nil && lastMessage.Role == "assistant"
+
+	// Check both model capability AND request preference
+	thinkingEnabled := thinkValue != nil && thinkValue.Bool()
+	// thinkingEnabled should be set to false for tools
+
+	if !thinkingEnabled {
+		p.state = CogitoCollectingContent
+		return
+	}
+
+	if prefill && lastMessage.Content != "" {
+		p.state = CogitoCollectingContent
+		return
+	}
+
+	// Note: for cogito, if there are tools, then we don't want to be thinking
+	if len(tools) > 0 {
+		p.state = CogitoCollectingContent
+		return
+	}
+
+	p.state = CogitoCollectingThinking
+}
+
+func (p *CogitoParser) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
+	p.setInitialState(lastMessage, tools, thinkValue)
+	return tools
+}
+
+type cogitoEvent interface {
+	isCogitoEvent()
+}
+
+type cogitoEventThinkingContent struct {
+	content string
+}
+
+type cogitoEventContent struct {
+	content string
+}
+
+type cogitoEventToolCall struct {
+	toolCall api.ToolCall
+}
+
+func (cogitoEventThinkingContent) isCogitoEvent() {}
+func (cogitoEventContent) isCogitoEvent()         {}
+func (cogitoEventToolCall) isCogitoEvent()        {}
+
+func (p *CogitoParser) Add(s string, done bool) (content string, thinking string, calls []api.ToolCall, err error) {
+	p.buffer.WriteString(s)
+	events := p.parseEvents()
+
+	var toolCalls []api.ToolCall
+	var contentSb strings.Builder
+	var thinkingSb strings.Builder
+	for _, event := range events {
+		switch event := event.(type) {
+		case cogitoEventToolCall:
+			toolCalls = append(toolCalls, event.toolCall)
+		case cogitoEventThinkingContent:
+			thinkingSb.WriteString(event.content)
+		case cogitoEventContent:
+			contentSb.WriteString(event.content)
+		}
+	}
+
+	return contentSb.String(), thinkingSb.String(), toolCalls, nil
+}
+
+func (p *CogitoParser) parseEvents() []cogitoEvent {
+	var all []cogitoEvent
+
+	keepLooping := true
+	for keepLooping {
+		var events []cogitoEvent
+		events, keepLooping = p.eat()
+		if len(events) > 0 {
+			all = append(all, events...)
+		}
+	}
+
+	return all
+}
+
+func (p *CogitoParser) eat() ([]cogitoEvent, bool) {
+	var events []cogitoEvent
+	bufStr := p.buffer.String()
+	if bufStr == "" {
+		return events, false
+	}
+
+	switch p.state {
+	case CogitoCollectingThinking:
+		if strings.Contains(bufStr, cogitoThinkingCloseTag) { // thinking[</think>] -> content
+			split := strings.SplitN(bufStr, cogitoThinkingCloseTag, 2)
+			thinking := split[0]
+			thinking = strings.TrimRightFunc(thinking, unicode.IsSpace)
+
+			remaining := split[1]
+			remaining = strings.TrimLeftFunc(remaining, unicode.IsSpace)
+
+			p.buffer.Reset()
+			p.buffer.WriteString(remaining)
+			p.state = CogitoCollectingContent
+
+			if len(thinking) > 0 {
+				events = append(events, cogitoEventThinkingContent{content: thinking})
+			}
+			return events, true
+		} else if overlapLen := overlap(bufStr, cogitoThinkingCloseTag); overlapLen > 0 { // partial </think>
+			beforePartialTag := bufStr[:len(bufStr)-overlapLen]
+			trailingLen := trailingWhitespaceLen(beforePartialTag)
+			ambiguousStart := len(beforePartialTag) - trailingLen
+
+			unambiguous := bufStr[:ambiguousStart]
+			ambiguous := bufStr[ambiguousStart:]
+			p.buffer.Reset()
+			p.buffer.WriteString(ambiguous)
+			if len(unambiguous) > 0 {
+				events = append(events, cogitoEventThinkingContent{content: unambiguous})
+			}
+			return events, false
+		} else { // otherwise its thinking content
+			whitespaceLen := trailingWhitespaceLen(bufStr)
+			ambiguousStart := len(bufStr) - whitespaceLen
+
+			unambiguous := bufStr[:ambiguousStart]
+			ambiguous := bufStr[ambiguousStart:]
+			p.buffer.Reset()
+			p.buffer.WriteString(ambiguous)
+			if len(unambiguous) > 0 {
+				events = append(events, cogitoEventThinkingContent{content: unambiguous})
+			}
+			return events, false
+		}
+
+	case CogitoCollectingContent:
+		switch {
+		case strings.Contains(bufStr, cogitoToolCallsBeginTag): // content[<｜tool▁calls▁begin｜>] -> tool calls
+			split := strings.SplitN(bufStr, cogitoToolCallsBeginTag, 2)
+			contentBefore := strings.TrimRightFunc(split[0], unicode.IsSpace)
+			remaining := split[1]
+
+			p.buffer.Reset()
+			p.buffer.WriteString(remaining)
+			p.state = CogitoCollectingToolCalls
+
+			if len(contentBefore) > 0 {
+				events = append(events, cogitoEventContent{content: contentBefore})
+			}
+			return events, true
+		case strings.Contains(bufStr, cogitoToolOutputsBeginTag): // content[<｜tool▁outputs▁begin｜>] -> tool outputs
+			split := strings.SplitN(bufStr, cogitoToolOutputsBeginTag, 2)
+			contentBefore := strings.TrimRightFunc(split[0], unicode.IsSpace)
+			remaining := split[1]
+
+			p.buffer.Reset()
+			p.buffer.WriteString(remaining)
+			p.state = CogitoCollectingToolOutput
+
+			if len(contentBefore) > 0 {
+				events = append(events, cogitoEventContent{content: contentBefore})
+			}
+			return events, true
+		default: // otherwise its content
+			p.buffer.Reset()
+			if len(bufStr) > 0 {
+				events = append(events, cogitoEventContent{content: bufStr})
+			}
+			return events, false
+		}
+	case CogitoCollectingToolCalls:
+		if idx := strings.Index(bufStr, cogitoToolCallBeginTag); idx != -1 {
+			startIdx := idx + len(cogitoToolCallBeginTag)
+			if endIdx := strings.Index(bufStr[startIdx:], cogitoToolCallEndTag); endIdx != -1 {
+				toolCallContent := bufStr[startIdx : startIdx+endIdx]
+
+				if toolCall, err := p.parseToolCallContent(toolCallContent); err == nil {
+					remaining := bufStr[startIdx+endIdx+len(cogitoToolCallEndTag):]
+					remaining = strings.TrimLeftFunc(remaining, unicode.IsSpace)
+
+					p.buffer.Reset()
+					p.buffer.WriteString(remaining)
+
+					events = append(events, cogitoEventToolCall{toolCall: toolCall})
+					return events, true
+				} else {
+					slog.Warn("cogito tool call parsing failed", "error", err)
+				}
+			}
+		}
+
+		if idx := strings.Index(bufStr, cogitoToolCallsEndTag); idx != -1 {
+			remaining := bufStr[idx+len(cogitoToolCallsEndTag):]
+			remaining = strings.TrimLeftFunc(remaining, unicode.IsSpace)
+
+			p.buffer.Reset()
+			p.buffer.WriteString(remaining)
+			p.state = CogitoCollectingContent
+
+			return events, true
+		}
+
+		return events, false
+
+	case CogitoCollectingToolOutput:
+		if idx := strings.Index(bufStr, cogitoToolOutputBeginTag); idx != -1 {
+			startIdx := idx + len(cogitoToolOutputBeginTag)
+			if endIdx := strings.Index(bufStr[startIdx:], cogitoToolOutputEndTag); endIdx != -1 {
+				remaining := bufStr[startIdx+endIdx+len(cogitoToolOutputEndTag):]
+				remaining = strings.TrimLeftFunc(remaining, unicode.IsSpace)
+
+				p.buffer.Reset()
+				p.buffer.WriteString(remaining)
+
+				return events, true
+			}
+		}
+
+		if idx := strings.Index(bufStr, cogitoToolOutputsEndTag); idx != -1 {
+			remaining := bufStr[idx+len(cogitoToolOutputsEndTag):]
+			remaining = strings.TrimLeftFunc(remaining, unicode.IsSpace)
+
+			p.buffer.Reset()
+			p.buffer.WriteString(remaining)
+			p.state = CogitoCollectingContent
+
+			return events, true
+		}
+
+		return events, false
+	}
+
+	return events, false
+}
+
+func (p *CogitoParser) parseToolCallContent(content string) (api.ToolCall, error) {
+	// Expected format: function<｜tool▁sep｜>tool_name\n```json\n{args}\n```
+	parts := strings.SplitN(content, cogitoToolSepTag, 2)
+	if len(parts) < 2 {
+		return api.ToolCall{}, errors.New("invalid format")
+	}
+	nameAndArgs := parts[1]
+
+	jsonStart := strings.Index(nameAndArgs, "\n```json\n")
+	if jsonStart == -1 {
+		return api.ToolCall{}, errors.New("invalid format")
+	}
+	toolName := strings.TrimSpace(nameAndArgs[:jsonStart])
+	jsonContent := nameAndArgs[jsonStart+len("\n```json\n"):]
+
+	jsonEnd := strings.Index(jsonContent, "\n```")
+	if jsonEnd == -1 {
+		return api.ToolCall{}, errors.New("invalid format")
+	}
+	argsJSON := jsonContent[:jsonEnd]
+
+	var args api.ToolCallFunctionArguments
+	if err := json.Unmarshal([]byte(argsJSON), &args); err != nil {
+		return api.ToolCall{}, err
+	}
+
+	return api.ToolCall{
+		Function: api.ToolCallFunction{
+			Name:      toolName,
+			Arguments: args,
+		},
+	}, nil
+}
--- a/model/parsers/cogito_test.go
+++ b/model/parsers/cogito_test.go
@@ -0,0 +1,565 @@
+package parsers
+
+import (
+	"strings"
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+
+	"github.com/ollama/ollama/api"
+)
+
+func TestCogitoParser(t *testing.T) {
+	tests := []struct {
+		name              string
+		input             string
+		expectedContent   string
+		expectedThinking  string
+		expectedToolCalls []api.ToolCall
+		tools             []api.Tool
+		lastMessage       *api.Message
+	}{
+		{
+			name:             "simple_content",
+			input:            "This is a simple response.",
+			expectedContent:  "This is a simple response.",
+			expectedThinking: "",
+		},
+		{
+			name:             "thinking_only",
+			input:            "This is thinking content.</think>This is response content.",
+			expectedContent:  "This is response content.",
+			expectedThinking: "This is thinking content.",
+		},
+		{
+			name: "tool_call_simple",
+			input: `<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather
+` + "```json\n" + `{"location":"Paris"}
+` + "```" + `<｜tool▁call▁end｜><｜tool▁calls▁end｜>`,
+			expectedToolCalls: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "get_weather",
+						Arguments: api.ToolCallFunctionArguments{
+							"location": "Paris",
+						},
+					},
+				},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name: "get_weather",
+						Parameters: api.ToolFunctionParameters{
+							Properties: map[string]api.ToolProperty{
+								"location": {Type: api.PropertyType{"string"}},
+							},
+						},
+					},
+				},
+			},
+		},
+		{
+			name: "thinking_with_tool_call",
+			input: `I need to check the weather.</think><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather
+` + "```json\n" + `{"location":"Paris"}
+` + "```" + `<｜tool▁call▁end｜><｜tool▁calls▁end｜>`,
+			expectedContent:  "I need to check the weather.</think>",
+			expectedThinking: "", // No thinking when tools are present (Cogito-specific behavior)
+			expectedToolCalls: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "get_weather",
+						Arguments: api.ToolCallFunctionArguments{
+							"location": "Paris",
+						},
+					},
+				},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name: "get_weather",
+						Parameters: api.ToolFunctionParameters{
+							Properties: map[string]api.ToolProperty{
+								"location": {Type: api.PropertyType{"string"}},
+							},
+						},
+					},
+				},
+			},
+		},
+		{
+			name: "multiple_tool_calls",
+			input: `<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather
+` + "```json\n" + `{"location":"Paris"}
+` + "```" + `<｜tool▁call▁end｜>
+<｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather
+` + "```json\n" + `{"location":"London"}
+` + "```" + `<｜tool▁call▁end｜><｜tool▁calls▁end｜>`,
+			expectedToolCalls: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "get_weather",
+						Arguments: api.ToolCallFunctionArguments{
+							"location": "Paris",
+						},
+					},
+				},
+				{
+					Function: api.ToolCallFunction{
+						Name: "get_weather",
+						Arguments: api.ToolCallFunctionArguments{
+							"location": "London",
+						},
+					},
+				},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name: "get_weather",
+						Parameters: api.ToolFunctionParameters{
+							Properties: map[string]api.ToolProperty{
+								"location": {Type: api.PropertyType{"string"}},
+							},
+						},
+					},
+				},
+			},
+		},
+		{
+			name: "complex_tool_arguments",
+			input: `<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>process_data
+` + "```json\n" + `{"items":["item1","item2"],"config":{"enabled":true,"threshold":0.95},"count":42}
+` + "```" + `<｜tool▁call▁end｜><｜tool▁calls▁end｜>`,
+			expectedToolCalls: []api.ToolCall{
+				{
+					Function: api.ToolCallFunction{
+						Name: "process_data",
+						Arguments: api.ToolCallFunctionArguments{
+							"items":  []any{"item1", "item2"},
+							"config": map[string]any{"enabled": true, "threshold": 0.95},
+							"count":  42.0,
+						},
+					},
+				},
+			},
+		},
+		{
+			name:             "tool_output_parsing",
+			input:            `<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>{"temperature": 22, "condition": "sunny"}<｜tool▁output▁end｜><｜tool▁outputs▁end｜>`,
+			expectedContent:  "",
+			expectedThinking: "",
+		},
+		{
+			name: "thinking_with_multiline_content",
+			input: `This is line 1
+This is line 2
+This is line 3</think>Final response here.`,
+			expectedContent:  "Final response here.",
+			expectedThinking: "This is line 1\nThis is line 2\nThis is line 3",
+		},
+		{
+			name:             "no_thinking_simple",
+			input:            "This is content.",
+			expectedContent:  "This is content.",
+			expectedThinking: "",
+		},
+		{
+			name:            "prefill_content_only",
+			input:           "Continuing from previous content.",
+			expectedContent: "Continuing from previous content.",
+			lastMessage: &api.Message{
+				Role:    "assistant",
+				Content: "Previous content",
+			},
+		},
+		{
+			name:             "prefill_with_thinking",
+			input:            "Continuing thinking</think>Continuing content.",
+			expectedContent:  "Continuing content.",
+			expectedThinking: "Continuing thinking",
+			lastMessage: &api.Message{
+				Role: "assistant",
+			},
+		},
+		// Edge cases
+		{
+			name:             "nested_think_tags_in_thinking",
+			input:            "I'm thinking <think>nested</think> more thinking</think>Final content.",
+			expectedContent:  "more thinking</think>Final content.",
+			expectedThinking: "I'm thinking <think>nested",
+		},
+		{
+			name:             "multiple_think_close_tags",
+			input:            "First thinking</think>Content</think>More content.",
+			expectedContent:  "Content</think>More content.",
+			expectedThinking: "First thinking",
+		},
+		{
+			name:             "empty_thinking_content",
+			input:            "</think>Just content here.",
+			expectedContent:  "</think>Just content here.",
+			expectedThinking: "",
+		},
+		{
+			name:             "thinking_disabled_with_think_tags",
+			input:            "Content with </think> tags should be treated as content.",
+			expectedContent:  "Content with </think> tags should be treated as content.",
+			expectedThinking: "",
+			lastMessage: &api.Message{
+				Role:    "assistant",
+				Content: "existing", // Forces non-thinking mode
+			},
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			// Use thinking-enabled parser for tests that expect thinking
+			hasThinking := tt.expectedThinking != ""
+			parser := &CogitoParser{}                                                  // it has thinking support
+			parser.Init(tt.tools, tt.lastMessage, &api.ThinkValue{Value: hasThinking}) // but we should set it with the request that the user wants
+
+			content, thinking, toolCalls, err := parser.Add(tt.input, true)
+			if err != nil {
+				t.Fatalf("Add() error = %v", err)
+			}
+
+			if diff := cmp.Diff(tt.expectedContent, content); diff != "" {
+				t.Errorf("content mismatch (-want +got):\n%s", diff)
+			}
+
+			if diff := cmp.Diff(tt.expectedThinking, thinking); diff != "" {
+				t.Errorf("thinking mismatch (-want +got):\n%s", diff)
+			}
+
+			if diff := cmp.Diff(tt.expectedToolCalls, toolCalls); diff != "" {
+				t.Errorf("tool calls mismatch (-want +got):\n%s", diff)
+			}
+		})
+	}
+}
+
+func TestCogitoParser_Streaming(t *testing.T) {
+	parser := &CogitoParser{}
+	parser.Init(nil, nil, &api.ThinkValue{Value: true})
+
+	chunks := []string{
+		"This is ",
+		"thinking content",
+		".</think>This is ",
+		"content.<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>test_tool\n```json\n{\"arg\":\"value\"}\n```<｜tool▁call▁end｜><｜tool▁calls▁end｜>",
+	}
+
+	var finalContent, finalThinking strings.Builder
+	var finalToolCalls []api.ToolCall
+
+	for i, chunk := range chunks {
+		done := i == len(chunks)-1
+		content, thinking, toolCalls, err := parser.Add(chunk, done)
+		if err != nil {
+			t.Fatalf("Add() error on chunk %d: %v", i, err)
+		}
+
+		finalContent.WriteString(content)
+		finalThinking.WriteString(thinking)
+		finalToolCalls = append(finalToolCalls, toolCalls...)
+	}
+
+	expectedContent := "This is content."
+	expectedThinking := "This is thinking content."
+	expectedToolCalls := []api.ToolCall{
+		{
+			Function: api.ToolCallFunction{
+				Name: "test_tool",
+				Arguments: api.ToolCallFunctionArguments{
+					"arg": "value",
+				},
+			},
+		},
+	}
+
+	if finalContent.String() != expectedContent {
+		t.Errorf("expected content %q, got %q", expectedContent, finalContent.String())
+	}
+
+	if finalThinking.String() != expectedThinking {
+		t.Errorf("expected thinking %q, got %q", expectedThinking, finalThinking.String())
+	}
+
+	if diff := cmp.Diff(expectedToolCalls, finalToolCalls); diff != "" {
+		t.Errorf("tool calls mismatch (-want +got):\n%s", diff)
+	}
+}
+
+func TestCogitoParser_StreamingEdgeCases(t *testing.T) {
+	tests := []struct {
+		name               string
+		chunks             []string
+		expectedContent    string
+		expectedThinking   string
+		expectedToolCalls  []api.ToolCall
+		hasThinkingSupport bool
+	}{
+		{
+			name: "split_thinking_tag",
+			chunks: []string{
+				"This is thinking content</thi",
+				"nk>This is content.",
+			},
+			expectedContent:    "This is content.",
+			expectedThinking:   "This is thinking content",
+			hasThinkingSupport: true,
+		},
+		{
+			name: "split_tool_calls_begin_tag_conservative_parsing",
+			chunks: []string{
+				"Content before<｜tool▁calls▁beg",
+				"in｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>test\n```json\n{}\n```<｜tool▁call▁end｜><｜tool▁calls▁end｜>",
+			},
+			// Parser is conservative - treats incomplete tags as content
+			expectedContent:    "Content before<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>test\n```json\n{}\n```<｜tool▁call▁end｜><｜tool▁calls▁end｜>",
+			expectedToolCalls:  nil,
+			hasThinkingSupport: false,
+		},
+		{
+			name: "thinking_disabled_with_split_tags",
+			chunks: []string{
+				"Content with </thi",
+				"nk> should be treated as content.",
+			},
+			expectedContent:    "Content with </think> should be treated as content.",
+			expectedThinking:   "",
+			hasThinkingSupport: false,
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			parser := &CogitoParser{}
+			parser.Init(nil, nil, &api.ThinkValue{Value: tt.hasThinkingSupport})
+
+			var finalContent, finalThinking strings.Builder
+			var finalToolCalls []api.ToolCall
+
+			for i, chunk := range tt.chunks {
+				done := i == len(tt.chunks)-1
+				content, thinking, toolCalls, err := parser.Add(chunk, done)
+				if err != nil {
+					t.Fatalf("Add() error on chunk %d: %v", i, err)
+				}
+
+				finalContent.WriteString(content)
+				finalThinking.WriteString(thinking)
+				finalToolCalls = append(finalToolCalls, toolCalls...)
+			}
+
+			if finalContent.String() != tt.expectedContent {
+				t.Errorf("expected content %q, got %q", tt.expectedContent, finalContent.String())
+			}
+
+			if finalThinking.String() != tt.expectedThinking {
+				t.Errorf("expected thinking %q, got %q", tt.expectedThinking, finalThinking.String())
+			}
+
+			if diff := cmp.Diff(tt.expectedToolCalls, finalToolCalls); diff != "" {
+				t.Errorf("tool calls mismatch (-want +got):\n%s", diff)
+			}
+		})
+	}
+}
+
+func TestCogitoParser_HasToolSupport(t *testing.T) {
+	parser := &CogitoParser{}
+	if !parser.HasToolSupport() {
+		t.Error("CogitoParser should support tools")
+	}
+}
+
+func TestCogitoParser_Init(t *testing.T) {
+	parser := &CogitoParser{}
+
+	tools := []api.Tool{
+		{Function: api.ToolFunction{Name: "test_tool"}},
+	}
+
+	lastMessage := &api.Message{Role: "assistant", Content: "previous"}
+
+	returnedTools := parser.Init(tools, lastMessage, nil)
+
+	if len(returnedTools) != len(tools) {
+		t.Errorf("expected %d tools returned, got %d", len(tools), len(returnedTools))
+	}
+}
+
+func TestCogitoParser_parseToolCallContent(t *testing.T) {
+	tests := []struct {
+		name        string
+		content     string
+		expected    api.ToolCall
+		expectError bool
+	}{
+		{
+			name: "valid_tool_call_standard_format",
+			content: `function<｜tool▁sep｜>get_weather
+` + "```json\n" + `{"location":"Paris"}
+` + "```",
+			expected: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name: "get_weather",
+					Arguments: api.ToolCallFunctionArguments{
+						"location": "Paris",
+					},
+				},
+			},
+			expectError: false,
+		},
+		{
+			name: "valid_tool_call_complex_args",
+			content: `function<｜tool▁sep｜>process_data
+` + "```json\n" + `{"items":["item1","item2"],"config":{"enabled":true},"count":42}
+` + "```",
+			expected: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name: "process_data",
+					Arguments: api.ToolCallFunctionArguments{
+						"items":  []any{"item1", "item2"},
+						"config": map[string]any{"enabled": true},
+						"count":  42.0,
+					},
+				},
+			},
+			expectError: false,
+		},
+		{
+			name: "valid_tool_call_empty_args",
+			content: `function<｜tool▁sep｜>no_args_tool
+` + "```json\n" + `{}
+` + "```",
+			expected: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name:      "no_args_tool",
+					Arguments: api.ToolCallFunctionArguments{},
+				},
+			},
+			expectError: false,
+		},
+		{
+			name:        "missing_separator",
+			content:     `functionget_weather` + "```json\n" + `{"location":"Paris"}` + "\n```",
+			expected:    api.ToolCall{},
+			expectError: true,
+		},
+		{
+			name:        "invalid_function_type",
+			content:     `not_function<｜tool▁sep｜>get_weather` + "```json\n" + `{"location":"Paris"}` + "\n```",
+			expected:    api.ToolCall{},
+			expectError: true,
+		},
+		{
+			name:        "missing_json_block_start",
+			content:     `function<｜tool▁sep｜>get_weather{"location":"Paris"}` + "```",
+			expected:    api.ToolCall{},
+			expectError: true,
+		},
+		{
+			name:        "missing_json_block_end",
+			content:     `function<｜tool▁sep｜>get_weather` + "```json\n" + `{"location":"Paris"}`,
+			expected:    api.ToolCall{},
+			expectError: true,
+		},
+		{
+			name:        "invalid_json",
+			content:     `function<｜tool▁sep｜>get_weather` + "```json\n" + `{location:Paris}` + "\n```",
+			expected:    api.ToolCall{},
+			expectError: true,
+		},
+		{
+			name:        "empty_function_type",
+			content:     `<｜tool▁sep｜>get_weather` + "```json\n" + `{"location":"Paris"}` + "\n```",
+			expected:    api.ToolCall{},
+			expectError: true,
+		},
+		{
+			name: "tool_with_spaces_in_name",
+			content: `function<｜tool▁sep｜>  get_weather  
+` + "```json\n" + `{"location":"Paris"}
+` + "```",
+			expected: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name: "get_weather",
+					Arguments: api.ToolCallFunctionArguments{
+						"location": "Paris",
+					},
+				},
+			},
+			expectError: false,
+		},
+		{
+			name: "tool_with_multiline_json",
+			content: `function<｜tool▁sep｜>get_weather
+` + "```json\n" + `{
+  "location": "Paris",
+  "units": "metric"
+}
+` + "```",
+			expected: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name: "get_weather",
+					Arguments: api.ToolCallFunctionArguments{
+						"location": "Paris",
+						"units":    "metric",
+					},
+				},
+			},
+			expectError: false,
+		},
+		{
+			name: "tool_with_nested_objects",
+			content: `function<｜tool▁sep｜>complex_tool
+` + "```json\n" + `{"nested":{"deep":{"value":123}}}
+` + "```",
+			expected: api.ToolCall{
+				Function: api.ToolCallFunction{
+					Name: "complex_tool",
+					Arguments: api.ToolCallFunctionArguments{
+						"nested": map[string]any{
+							"deep": map[string]any{
+								"value": 123.0,
+							},
+						},
+					},
+				},
+			},
+			expectError: false,
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			parser := &CogitoParser{}
+
+			result, err := parser.parseToolCallContent(tt.content)
+
+			if tt.expectError {
+				if err == nil {
+					t.Errorf("expected error but got none")
+				}
+				return
+			}
+
+			if err != nil {
+				t.Fatalf("unexpected error: %v", err)
+			}
+
+			if diff := cmp.Diff(tt.expected, result); diff != "" {
+				t.Errorf("tool call mismatch (-want +got):\n%s", diff)
+			}
+		})
+	}
+}
--- a/model/parsers/parsers.go
+++ b/model/parsers/parsers.go
@@ -6,9 +6,9 @@ import (
 )

 type Parser interface {
-	// Init initializes the parser with tools and optional last message for chat prefill
+	// Init initializes the parser with tools, optional last message for chat prefill, and think value
 	// Returns processed tools if the parser needs to modify them (e.g., harmony renames them)
-	Init(tools []api.Tool, lastMessage *api.Message) []api.Tool
+	Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool
 	// Add processes streamed content and returns parsed content, thinking, and tool calls
 	// The done flag indicates if this is the last chunk (used for draining accumulators)
 	Add(s string, done bool) (content string, thinking string, calls []api.ToolCall, err error)
@@ -52,6 +52,8 @@ func ParserForName(name string) Parser {
 		return &PassthroughParser{}
 	case "harmony":
 		return harmony.NewHarmonyMessageHandler()
+	case "cogito":
+		return &CogitoParser{}
 	default:
 		return nil
 	}
@@ -59,7 +61,7 @@ func ParserForName(name string) Parser {

 type PassthroughParser struct{}

-func (p *PassthroughParser) Init(tools []api.Tool, lastMessage *api.Message) []api.Tool {
+func (p *PassthroughParser) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
 	return tools // passthrough doesn't modify tools
 }

--- a/model/parsers/parsers_test.go
+++ b/model/parsers/parsers_test.go
@@ -10,7 +10,7 @@ type mockParser struct {
 	name string
 }

-func (m *mockParser) Init(tools []api.Tool, lastMessage *api.Message) []api.Tool {
+func (m *mockParser) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
 	return tools
 }

--- a/model/parsers/qwen3coder.go
+++ b/model/parsers/qwen3coder.go
@@ -43,7 +43,7 @@ func (p *Qwen3CoderParser) HasThinkingSupport() bool {
 	return false
 }

-func (p *Qwen3CoderParser) Init(tools []api.Tool, lastMessage *api.Message) []api.Tool {
+func (p *Qwen3CoderParser) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
 	p.tools = tools
 	return tools // Qwen doesn't modify tools
 }
@@ -432,7 +432,7 @@ func transformToXML(raw string) string {
 		groups := qwenTagRegex.FindStringSubmatch(match)
 		tag := groups[1]
 		var escapedValue strings.Builder
-		xml.EscapeText(&escapedValue, []byte(groups[2]))
+		_ = xml.EscapeText(&escapedValue, []byte(groups[2])) // error is always nil for strings.Builder
 		return fmt.Sprintf(`<%s name="%s">`, tag, escapedValue.String())
 	})

--- a/model/parsers/qwen3vl.go
+++ b/model/parsers/qwen3vl.go
@@ -54,7 +54,7 @@ func (p *Qwen3VLParser) setInitialState(lastMessage *api.Message) {
 	p.state = CollectingThinkingContent
 }

-func (p *Qwen3VLParser) Init(tools []api.Tool, lastMessage *api.Message) []api.Tool {
+func (p *Qwen3VLParser) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
 	p.tools = tools
 	p.setInitialState(lastMessage)
 	return tools
--- a/model/parsers/qwen3vl_nonthinking_test.go
+++ b/model/parsers/qwen3vl_nonthinking_test.go
@@ -198,7 +198,7 @@ func TestQwen3VLNonThinkingParserStreaming(t *testing.T) {

 		t.Run(tc.desc, func(t *testing.T) {
 			parser := Qwen3VLParser{hasThinkingSupport: false}
-			parser.Init([]api.Tool{}, nil)
+			parser.Init([]api.Tool{}, nil, nil)

 			for i, step := range tc.steps {
 				parser.buffer.WriteString(step.input)
@@ -515,7 +515,7 @@ func TestQwenOldParserStreaming(t *testing.T) {

 		t.Run(tc.desc, func(t *testing.T) {
 			parser := Qwen3VLParser{hasThinkingSupport: false}
-			parser.Init([]api.Tool{}, nil)
+			parser.Init([]api.Tool{}, nil, nil)

 			for i, step := range tc.steps {
 				parser.buffer.WriteString(step.input)
@@ -822,7 +822,7 @@ func TestQwen3VLNonThinkingToolCallWhitespaceHandling(t *testing.T) {

 		t.Run(tc.desc, func(t *testing.T) {
 			parser := Qwen3VLParser{hasThinkingSupport: false}
-			parser.Init([]api.Tool{}, nil)
+			parser.Init([]api.Tool{}, nil, nil)

 			for i, step := range tc.steps {
 				parser.buffer.WriteString(step.input)
--- a/model/parsers/qwen3vl_thinking_test.go
+++ b/model/parsers/qwen3vl_thinking_test.go
@@ -205,7 +205,7 @@ func TestQwen3VLThinkingParserStreaming(t *testing.T) {

 		t.Run(tc.desc, func(t *testing.T) {
 			parser := Qwen3VLParser{hasThinkingSupport: true}
-			parser.Init([]api.Tool{}, nil)
+			parser.Init([]api.Tool{}, nil, nil)
 			// parser.state = CollectingThinkingContent

 			for i, step := range tc.steps {
@@ -386,7 +386,7 @@ func TestQwen3VLParserState(t *testing.T) {

 	for _, tc := range cases {
 		parser := Qwen3VLParser{hasThinkingSupport: tc.hasThinking}
-		parser.Init(nil, tc.last)
+		parser.Init(nil, tc.last, nil)
 		if parser.state != tc.wantState {
 			t.Errorf("%s: got state %v, want %v", tc.desc, parser.state, tc.wantState)
 		}
@@ -437,7 +437,7 @@ func TestQwen3VLThinkingParserWithThinkingPrefill(t *testing.T) {
 	for _, tc := range cases {
 		t.Run(tc.desc, func(t *testing.T) {
 			parser := Qwen3VLParser{hasThinkingSupport: true}
-			parser.Init([]api.Tool{}, last)
+			parser.Init([]api.Tool{}, last, nil)

 			for i, step := range tc.steps {
 				parser.buffer.WriteString(step.input)
@@ -500,7 +500,7 @@ func TestQwen3VLThinkingParserWithNonThinkingPrefill(t *testing.T) {
 	for _, tc := range cases {
 		t.Run(tc.desc, func(t *testing.T) {
 			parser := Qwen3VLParser{hasThinkingSupport: true}
-			parser.Init([]api.Tool{}, last)
+			parser.Init([]api.Tool{}, last, nil)

 			for i, step := range tc.steps {
 				parser.buffer.WriteString(step.input)
@@ -523,7 +523,7 @@ func TestQwen3VLThinkingParserStreamingAssistantPrefillContent(t *testing.T) {
 	// last message is assistant with content ⇒ start in CollectingContent
 	last := &api.Message{Role: "assistant", Content: "has content"}
 	parser := Qwen3VLParser{hasThinkingSupport: true}
-	parser.Init([]api.Tool{}, last)
+	parser.Init([]api.Tool{}, last, nil)

 	type step struct {
 		input      string
@@ -750,7 +750,7 @@ func TestQwen3VLThinkingWhitespaceHandling(t *testing.T) {

 		t.Run(tc.desc, func(t *testing.T) {
 			parser := Qwen3VLParser{hasThinkingSupport: true}
-			parser.Init([]api.Tool{}, nil)
+			parser.Init([]api.Tool{}, nil, nil)

 			for i, step := range tc.steps {
 				parser.buffer.WriteString(step.input)
@@ -859,7 +859,7 @@ func TestQwen3VLToolCallWhitespaceHandling(t *testing.T) {

 		t.Run(tc.desc, func(t *testing.T) {
 			parser := Qwen3VLParser{hasThinkingSupport: true}
-			parser.Init([]api.Tool{}, tc.prefillMsg)
+			parser.Init([]api.Tool{}, tc.prefillMsg, nil)

 			for i, step := range tc.steps {
 				parser.buffer.WriteString(step.input)
--- a/model/renderers/cogito.go
+++ b/model/renderers/cogito.go
@@ -0,0 +1,129 @@
+package renderers
+
+import (
+	"encoding/json"
+	"strings"
+
+	"github.com/ollama/ollama/api"
+)
+
+type CogitoRenderer struct {
+	isThinking bool
+}
+
+func (r *CogitoRenderer) Render(messages []api.Message, tools []api.Tool, thinkValue *api.ThinkValue) (string, error) {
+	var sb strings.Builder
+
+	defaultPrompt := "You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco."
+
+	// thinking is enabled: model must support it AND user must request it (true)
+	enableThinking := r.isThinking && (thinkValue != nil && thinkValue.Bool())
+
+	var systemPrompt string
+	var conversationMessages []api.Message
+
+	if len(messages) > 0 && messages[0].Role == "system" {
+		systemPrompt = messages[0].Content
+		conversationMessages = messages[1:]
+	} else {
+		conversationMessages = messages
+	}
+
+	var finalSystemPrompt string
+	if enableThinking {
+		finalSystemPrompt = "Enable deep thinking subroutine.\n\n" + defaultPrompt
+		if systemPrompt != "" {
+			finalSystemPrompt += "\n\n" + systemPrompt + "\n\n"
+		}
+	} else {
+		finalSystemPrompt = defaultPrompt
+		if systemPrompt != "" {
+			finalSystemPrompt += "\n\n" + systemPrompt
+		}
+	}
+
+	if len(tools) > 0 {
+		if finalSystemPrompt != "" {
+			finalSystemPrompt += "\nYou have the following functions available:\n"
+		} else {
+			finalSystemPrompt = "You have the following functions available:\n"
+		}
+
+		for _, tool := range tools {
+			toolJSON, _ := json.MarshalIndent(tool, "", "    ") // TODO(gguo): double check json format
+			finalSystemPrompt += "```json\n" + string(toolJSON) + "\n```\n"
+		}
+	}
+
+	sb.WriteString("<｜begin▁of▁sentence｜>" + finalSystemPrompt)
+
+	outputsOpen := false
+	isLastUser := false
+
+	for i, message := range conversationMessages {
+		switch message.Role {
+		case "user":
+			isLastUser = true
+			sb.WriteString("<｜User｜>" + message.Content + "<｜Assistant｜>")
+
+		case "assistant":
+			isLastUser = false
+
+			if len(message.ToolCalls) > 0 {
+				if message.Content != "" {
+					sb.WriteString(message.Content)
+				}
+
+				sb.WriteString("<｜tool▁calls▁begin｜>")
+
+				for j, toolCall := range message.ToolCalls {
+					sb.WriteString("<｜tool▁call▁begin｜>function<｜tool▁sep｜>" + toolCall.Function.Name)
+
+					argsJSON, _ := json.Marshal(toolCall.Function.Arguments)
+					sb.WriteString("\n```json\n" + string(argsJSON) + "\n```")
+					sb.WriteString("<｜tool▁call▁end｜>")
+
+					if j < len(message.ToolCalls)-1 {
+						sb.WriteString("\n")
+					}
+				}
+
+				sb.WriteString("<｜tool▁calls▁end｜><｜end▁of▁sentence｜>")
+			} else {
+				sb.WriteString(message.Content + "<｜end▁of▁sentence｜>")
+			}
+
+		case "tool":
+			isLastUser = false
+
+			if !outputsOpen {
+				sb.WriteString("<｜tool▁outputs▁begin｜>")
+				outputsOpen = true
+			}
+
+			sb.WriteString("<｜tool▁output▁begin｜>" + message.Content + "<｜tool▁output▁end｜>")
+
+			hasNextTool := i+1 < len(conversationMessages) && conversationMessages[i+1].Role == "tool"
+			if hasNextTool {
+				sb.WriteString("\n")
+			} else {
+				sb.WriteString("<｜tool▁outputs▁end｜>")
+				outputsOpen = false
+			}
+		}
+	}
+
+	if outputsOpen {
+		sb.WriteString("<｜tool▁outputs▁end｜>")
+	}
+
+	if !isLastUser {
+		sb.WriteString("<｜Assistant｜>")
+	}
+
+	if enableThinking {
+		sb.WriteString("<think>\n")
+	}
+
+	return sb.String(), nil
+}
--- a/model/renderers/cogito_test.go
+++ b/model/renderers/cogito_test.go
@@ -0,0 +1,491 @@
+package renderers
+
+import (
+	"testing"
+
+	"github.com/google/go-cmp/cmp"
+
+	"github.com/ollama/ollama/api"
+)
+
+func TestCogitoRenderer(t *testing.T) {
+	tests := []struct {
+		name       string
+		messages   []api.Message
+		tools      []api.Tool
+		thinkValue *api.ThinkValue
+		expected   string
+	}{
+		{
+			name: "basic user message",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello, how are you?"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<｜begin▁of▁sentence｜>You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.<｜User｜>Hello, how are you?<｜Assistant｜>`,
+		},
+		{
+			name: "basic with system message",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a helpful assistant."},
+				{Role: "user", Content: "Hello, how are you?"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected: `<｜begin▁of▁sentence｜>You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.
+
+You are a helpful assistant.<｜User｜>Hello, how are you?<｜Assistant｜>`,
+		},
+		{
+			name: "conversation with assistant response",
+			messages: []api.Message{
+				{Role: "user", Content: "What is the capital of France?"},
+				{Role: "assistant", Content: "The capital of France is Paris."},
+				{Role: "user", Content: "Fantastic!"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<｜begin▁of▁sentence｜>You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.<｜User｜>What is the capital of France?<｜Assistant｜>The capital of France is Paris.<｜end▁of▁sentence｜><｜User｜>Fantastic!<｜Assistant｜>`,
+		},
+		{
+			name: "thinking enabled without system",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello, how are you?"},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected: `<｜begin▁of▁sentence｜>Enable deep thinking subroutine.
+
+You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.<｜User｜>Hello, how are you?<｜Assistant｜><think>
+`,
+		},
+		{
+			name: "thinking enabled with system",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a helpful assistant."},
+				{Role: "user", Content: "Hello, how are you?"},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected: `<｜begin▁of▁sentence｜>Enable deep thinking subroutine.
+
+You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.
+
+You are a helpful assistant.
+
+<｜User｜>Hello, how are you?<｜Assistant｜><think>
+`,
+		},
+		{
+			name: "thinking disabled",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello, how are you?"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<｜begin▁of▁sentence｜>You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.<｜User｜>Hello, how are you?<｜Assistant｜>`,
+		},
+		{
+			name: "with tools",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather like?"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get current weather",
+						Parameters: api.ToolFunctionParameters{
+							Type: "object",
+							Properties: map[string]api.ToolProperty{
+								"location": {
+									Type:        api.PropertyType{"string"},
+									Description: "City name",
+								},
+							},
+							Required: []string{"location"},
+						},
+					},
+				},
+			},
+			expected: `<｜begin▁of▁sentence｜>You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.
+You have the following functions available:
+` + "```json\n" + `{
+    "type": "function",
+    "function": {
+        "name": "get_weather",
+        "description": "Get current weather",
+        "parameters": {
+            "type": "object",
+            "required": [
+                "location"
+            ],
+            "properties": {
+                "location": {
+                    "type": "string",
+                    "description": "City name"
+                }
+            }
+        }
+    }
+}
+` + "```\n" + `<｜User｜>What's the weather like?<｜Assistant｜>`,
+		},
+		{
+			name: "assistant with tool calls",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather in Paris?"},
+				{
+					Role:    "assistant",
+					Content: "I'll check the weather in Paris for you.",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: api.ToolCallFunctionArguments{
+									"location": "Paris",
+								},
+							},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected: `<｜begin▁of▁sentence｜>You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.<｜User｜>What's the weather in Paris?<｜Assistant｜>I'll check the weather in Paris for you.<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather
+` + "```json\n" + `{"location":"Paris"}
+` + "```" + `<｜tool▁call▁end｜><｜tool▁calls▁end｜><｜end▁of▁sentence｜><｜Assistant｜>`,
+		},
+		{
+			name: "tool response",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather in Paris?"},
+				{
+					Role: "assistant",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: api.ToolCallFunctionArguments{
+									"location": "Paris",
+								},
+							},
+						},
+					},
+				},
+				{Role: "tool", Content: "Temperature: 22°C, Sunny"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected: `<｜begin▁of▁sentence｜>You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.<｜User｜>What's the weather in Paris?<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather
+` + "```json\n" + `{"location":"Paris"}
+` + "```" + `<｜tool▁call▁end｜><｜tool▁calls▁end｜><｜end▁of▁sentence｜><｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>Temperature: 22°C, Sunny<｜tool▁output▁end｜><｜tool▁outputs▁end｜><｜Assistant｜>`,
+		},
+		{
+			name: "multiple tool responses",
+			messages: []api.Message{
+				{Role: "user", Content: "Get weather for Paris and London"},
+				{
+					Role: "assistant",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: api.ToolCallFunctionArguments{
+									"location": "Paris",
+								},
+							},
+						},
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: api.ToolCallFunctionArguments{
+									"location": "London",
+								},
+							},
+						},
+					},
+				},
+				{Role: "tool", Content: "Paris: 22°C, Sunny"},
+				{Role: "tool", Content: "London: 18°C, Cloudy"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected: `<｜begin▁of▁sentence｜>You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.<｜User｜>Get weather for Paris and London<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather
+` + "```json\n" + `{"location":"Paris"}
+` + "```" + `<｜tool▁call▁end｜>
+<｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather
+` + "```json\n" + `{"location":"London"}
+` + "```" + `<｜tool▁call▁end｜><｜tool▁calls▁end｜><｜end▁of▁sentence｜><｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>Paris: 22°C, Sunny<｜tool▁output▁end｜>
+<｜tool▁output▁begin｜>London: 18°C, Cloudy<｜tool▁output▁end｜><｜tool▁outputs▁end｜><｜Assistant｜>`,
+		},
+		{
+			name: "thinking with tools",
+			messages: []api.Message{
+				{Role: "user", Content: "What's the weather like?"},
+			},
+			tools: []api.Tool{
+				{
+					Type: "function",
+					Function: api.ToolFunction{
+						Name:        "get_weather",
+						Description: "Get current weather",
+						Parameters: api.ToolFunctionParameters{
+							Type: "object",
+							Properties: map[string]api.ToolProperty{
+								"location": {
+									Type:        api.PropertyType{"string"},
+									Description: "City name",
+								},
+							},
+							Required: []string{"location"},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected: `<｜begin▁of▁sentence｜>Enable deep thinking subroutine.
+
+You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.
+You have the following functions available:
+` + "```json\n" + `{
+    "type": "function",
+    "function": {
+        "name": "get_weather",
+        "description": "Get current weather",
+        "parameters": {
+            "type": "object",
+            "required": [
+                "location"
+            ],
+            "properties": {
+                "location": {
+                    "type": "string",
+                    "description": "City name"
+                }
+            }
+        }
+    }
+}
+` + "```\n" + `<｜User｜>What's the weather like?<｜Assistant｜><think>
+`,
+		},
+		// test cases based on cogito
+		{
+			name: "single_turn_thinking_false",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<｜begin▁of▁sentence｜>You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.<｜User｜>Hello<｜Assistant｜>`,
+		},
+		{
+			name: "single_turn_thinking_true",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello"},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected: `<｜begin▁of▁sentence｜>Enable deep thinking subroutine.
+
+You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.<｜User｜>Hello<｜Assistant｜><think>
+`,
+		},
+		{
+			name: "multi_turn_thinking_false",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello"},
+				{Role: "assistant", Content: "Hi there!"},
+				{Role: "user", Content: "How are you?"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<｜begin▁of▁sentence｜>You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.<｜User｜>Hello<｜Assistant｜>Hi there!<｜end▁of▁sentence｜><｜User｜>How are you?<｜Assistant｜>`,
+		},
+		{
+			name: "multi_turn_thinking_true",
+			messages: []api.Message{
+				{Role: "user", Content: "Hello"},
+				{Role: "assistant", Content: "Hi there!"},
+				{Role: "user", Content: "How are you?"},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected: `<｜begin▁of▁sentence｜>Enable deep thinking subroutine.
+
+You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.<｜User｜>Hello<｜Assistant｜>Hi there!<｜end▁of▁sentence｜><｜User｜>How are you?<｜Assistant｜><think>
+`,
+		},
+		{
+			name: "multi_with_system_thinking_false",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a helpful assistant"},
+				{Role: "user", Content: "Start"},
+				{Role: "assistant", Content: "Okay"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected: `<｜begin▁of▁sentence｜>You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.
+
+You are a helpful assistant<｜User｜>Start<｜Assistant｜>Okay<｜end▁of▁sentence｜><｜Assistant｜>`,
+		},
+		{
+			name: "multi_with_system_thinking_true",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a helpful assistant"},
+				{Role: "user", Content: "Start"},
+				{Role: "assistant", Content: "Okay"},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected: `<｜begin▁of▁sentence｜>Enable deep thinking subroutine.
+
+You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.
+
+You are a helpful assistant
+
+<｜User｜>Start<｜Assistant｜>Okay<｜end▁of▁sentence｜><｜Assistant｜><think>
+`,
+		},
+		{
+			name: "multi_with_system2_thinking_false",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a pirate chatbot who always responds in pirate speak!"},
+				{Role: "user", Content: "Give me a short introduction to LLMs."},
+				{Role: "assistant", Content: "Arrr! I'm a pirate"},
+				{Role: "user", Content: "Tell me more about LLMs."},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected: `<｜begin▁of▁sentence｜>You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.
+
+You are a pirate chatbot who always responds in pirate speak!<｜User｜>Give me a short introduction to LLMs.<｜Assistant｜>Arrr! I'm a pirate<｜end▁of▁sentence｜><｜User｜>Tell me more about LLMs.<｜Assistant｜>`,
+		},
+		{
+			name: "multi_with_system2_thinking_true",
+			messages: []api.Message{
+				{Role: "system", Content: "You are a pirate chatbot who always responds in pirate speak!"},
+				{Role: "user", Content: "Give me a short introduction to LLMs."},
+				{Role: "assistant", Content: "Arrr! I'm a pirate"},
+				{Role: "user", Content: "Tell me more about LLMs."},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected: `<｜begin▁of▁sentence｜>Enable deep thinking subroutine.
+
+You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.
+
+You are a pirate chatbot who always responds in pirate speak!
+
+<｜User｜>Give me a short introduction to LLMs.<｜Assistant｜>Arrr! I'm a pirate<｜end▁of▁sentence｜><｜User｜>Tell me more about LLMs.<｜Assistant｜><think>
+`,
+		},
+		// tools
+		{
+			name: "tool_calls_only_no_content",
+			messages: []api.Message{
+				{Role: "user", Content: "Get weather for Paris"},
+				{
+					Role: "assistant",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "get_weather",
+								Arguments: api.ToolCallFunctionArguments{
+									"location": "Paris",
+								},
+							},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected: `<｜begin▁of▁sentence｜>You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.<｜User｜>Get weather for Paris<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>get_weather
+` + "```json\n" + `{"location":"Paris"}
+` + "```" + `<｜tool▁call▁end｜><｜tool▁calls▁end｜><｜end▁of▁sentence｜><｜Assistant｜>`,
+		},
+		{
+			name: "complex_tool_arguments",
+			messages: []api.Message{
+				{Role: "user", Content: "Process complex data"},
+				{
+					Role: "assistant",
+					ToolCalls: []api.ToolCall{
+						{
+							Function: api.ToolCallFunction{
+								Name: "process_data",
+								Arguments: api.ToolCallFunctionArguments{
+									"items": []any{"item1", "item2", "item3"},
+									"config": map[string]any{
+										"enabled":   true,
+										"threshold": 0.95,
+										"tags":      []string{"important", "urgent"},
+									},
+								},
+							},
+						},
+					},
+				},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected: `<｜begin▁of▁sentence｜>You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.<｜User｜>Process complex data<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>process_data
+` + "```json\n" + `{"config":{"enabled":true,"tags":["important","urgent"],"threshold":0.95},"items":["item1","item2","item3"]}
+` + "```" + `<｜tool▁call▁end｜><｜tool▁calls▁end｜><｜end▁of▁sentence｜><｜Assistant｜>`,
+		},
+		{
+			name: "empty_messages",
+			messages: []api.Message{
+				{Role: "system", Content: ""},
+				{Role: "user", Content: "Hello"},
+				{Role: "assistant", Content: ""},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<｜begin▁of▁sentence｜>You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.<｜User｜>Hello<｜Assistant｜><｜end▁of▁sentence｜><｜Assistant｜>`,
+		},
+		{
+			name: "thinking_with_empty_assistant_content",
+			messages: []api.Message{
+				{Role: "user", Content: "Think about this"},
+				{Role: "assistant", Content: ""},
+			},
+			thinkValue: &api.ThinkValue{Value: true},
+			expected: `<｜begin▁of▁sentence｜>Enable deep thinking subroutine.
+
+You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.<｜User｜>Think about this<｜Assistant｜><｜end▁of▁sentence｜><｜Assistant｜><think>
+`,
+		},
+		{
+			name: "multiple_system_messages",
+			messages: []api.Message{
+				{Role: "system", Content: "First instruction"},
+				{Role: "system", Content: "Second instruction"},
+				{Role: "user", Content: "Hello"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected: `<｜begin▁of▁sentence｜>You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.
+
+First instruction<｜User｜>Hello<｜Assistant｜>`,
+		},
+		{
+			name: "special_characters_in_content",
+			messages: []api.Message{
+				{Role: "user", Content: "What about <|special|> tokens and \"quotes\"?"},
+				{Role: "assistant", Content: "They're handled normally in content."},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<｜begin▁of▁sentence｜>You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.<｜User｜>What about <|special|> tokens and "quotes"?<｜Assistant｜>They're handled normally in content.<｜end▁of▁sentence｜><｜Assistant｜>`,
+		},
+		{
+			name: "long_conversation_multiple_rounds",
+			messages: []api.Message{
+				{Role: "user", Content: "Hi"},
+				{Role: "assistant", Content: "Hello!"},
+				{Role: "user", Content: "How are you?"},
+				{Role: "assistant", Content: "Good, thanks!"},
+				{Role: "user", Content: "What's the weather?"},
+			},
+			thinkValue: &api.ThinkValue{Value: false},
+			expected:   `<｜begin▁of▁sentence｜>You are Cogito, an AI assistant created by Deep Cogito, which is an AI research lab based in San Francisco.<｜User｜>Hi<｜Assistant｜>Hello!<｜end▁of▁sentence｜><｜User｜>How are you?<｜Assistant｜>Good, thanks!<｜end▁of▁sentence｜><｜User｜>What's the weather?<｜Assistant｜>`,
+		},
+	}
+
+	renderer := &CogitoRenderer{isThinking: true}
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			rendered, err := renderer.Render(tt.messages, tt.tools, tt.thinkValue)
+			if err != nil {
+				t.Fatalf("Render() error = %v", err)
+			}
+			if diff := cmp.Diff(tt.expected, rendered); diff != "" {
+				t.Errorf("Render() mismatch (-want +got):\n%s", diff)
+			}
+		})
+	}
+}
--- a/model/renderers/renderer.go
+++ b/model/renderers/renderer.go
@@ -56,6 +56,9 @@ func rendererForName(name string) Renderer {
 	case "qwen3-vl-thinking":
 		renderer := &Qwen3VLRenderer{isThinking: true, useImgTags: RenderImgTags}
 		return renderer
+	case "cogito":
+		renderer := &CogitoRenderer{isThinking: true}
+		return renderer
 	default:
 		return nil
 	}
--- a/model/wordpiece.go
+++ b/model/wordpiece.go
@@ -10,7 +10,8 @@ import (
 )

 type WordPiece struct {
-	vocab *Vocabulary
+	vocab     *Vocabulary
+	lowercase bool
 }

 // ggmlPrefix is the prefix used by GGML vocabularies to indicate word boundaries.
@@ -114,8 +115,10 @@ func (wpm WordPiece) Encode(s string, addSpecial bool) ([]int32, error) {
 					subword = ggmlPrefix + subword
 				}

-				// TODO: some models might not want [ToLower]
-				piece = wpm.vocab.Encode(strings.ToLower(subword))
+				if wpm.lowercase {
+					subword = strings.ToLower(subword)
+				}
+				piece = wpm.vocab.Encode(subword)
 				if piece >= 0 {
 					break
 				}
@@ -160,8 +163,9 @@ func (wpm WordPiece) Vocabulary() *Vocabulary {

 var _ TextProcessor = (*WordPiece)(nil)

-func NewWordPiece(vocab *Vocabulary) WordPiece {
+func NewWordPiece(vocab *Vocabulary, lowercase bool) WordPiece {
 	return WordPiece{
-		vocab: vocab,
+		vocab:     vocab,
+		lowercase: lowercase,
 	}
 }
--- a/model/wordpiece_test.go
+++ b/model/wordpiece_test.go
@@ -15,7 +15,9 @@ func TestWordPiece(t *testing.T) {
 			AddEOS: true,
 			BOS:    []int32{1},
 			EOS:    []int32{2},
-		})
+		},
+		true, // lowercase
+	)

 	ids, err := wpm.Encode("Hello world!", true)
 	if err != nil {
--- a/parser/expandpath_test.go
+++ b/parser/expandpath_test.go
@@ -0,0 +1,123 @@
+package parser
+
+import (
+	"os"
+	"os/user"
+	"path/filepath"
+	"runtime"
+	"testing"
+)
+
+func TestExpandPath(t *testing.T) {
+	mockCurrentUser := func() (*user.User, error) {
+		return &user.User{
+			Username: "testuser",
+			HomeDir: func() string {
+				if os.PathSeparator == '\\' {
+					return filepath.FromSlash("D:/home/testuser")
+				}
+				return "/home/testuser"
+			}(),
+		}, nil
+	}
+
+	mockLookupUser := func(username string) (*user.User, error) {
+		fakeUsers := map[string]string{
+			"testuser": func() string {
+				if os.PathSeparator == '\\' {
+					return filepath.FromSlash("D:/home/testuser")
+				}
+				return "/home/testuser"
+			}(),
+			"anotheruser": func() string {
+				if os.PathSeparator == '\\' {
+					return filepath.FromSlash("D:/home/anotheruser")
+				}
+				return "/home/anotheruser"
+			}(),
+		}
+
+		if homeDir, ok := fakeUsers[username]; ok {
+			return &user.User{
+				Username: username,
+				HomeDir:  homeDir,
+			}, nil
+		}
+		return nil, os.ErrNotExist
+	}
+
+	pwd, err := os.Getwd()
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	t.Run("unix tests", func(t *testing.T) {
+		if runtime.GOOS == "windows" {
+			return
+		}
+
+		tests := []struct {
+			path        string
+			relativeDir string
+			expected    string
+			shouldErr   bool
+		}{
+			{"~", "", "/home/testuser", false},
+			{"~/myfolder/myfile.txt", "", "/home/testuser/myfolder/myfile.txt", false},
+			{"~anotheruser/docs/file.txt", "", "/home/anotheruser/docs/file.txt", false},
+			{"~nonexistentuser/file.txt", "", "", true},
+			{"relative/path/to/file", "", filepath.Join(pwd, "relative/path/to/file"), false},
+			{"/absolute/path/to/file", "", "/absolute/path/to/file", false},
+			{"/absolute/path/to/file", "someotherdir/", "/absolute/path/to/file", false},
+			{".", pwd, pwd, false},
+			{".", "", pwd, false},
+			{"somefile", "somedir", filepath.Join(pwd, "somedir", "somefile"), false},
+		}
+
+		for _, test := range tests {
+			result, err := expandPathImpl(test.path, test.relativeDir, mockCurrentUser, mockLookupUser)
+			if (err != nil) != test.shouldErr {
+				t.Errorf("expandPathImpl(%q) returned error: %v, expected error: %v", test.path, err != nil, test.shouldErr)
+			}
+
+			if result != test.expected && !test.shouldErr {
+				t.Errorf("expandPathImpl(%q) = %q, want %q", test.path, result, test.expected)
+			}
+		}
+	})
+
+	t.Run("windows tests", func(t *testing.T) {
+		if runtime.GOOS != "windows" {
+			return
+		}
+
+		tests := []struct {
+			path        string
+			relativeDir string
+			expected    string
+			shouldErr   bool
+		}{
+			{"~", "", "D:\\home\\testuser", false},
+			{"~/myfolder/myfile.txt", "", "D:\\home\\testuser\\myfolder\\myfile.txt", false},
+			{"~anotheruser/docs/file.txt", "", "D:\\home\\anotheruser\\docs\\file.txt", false},
+			{"~nonexistentuser/file.txt", "", "", true},
+			{"relative\\path\\to\\file", "", filepath.Join(pwd, "relative\\path\\to\\file"), false},
+			{"D:\\absolute\\path\\to\\file", "", "D:\\absolute\\path\\to\\file", false},
+			{"D:\\absolute\\path\\to\\file", "someotherdir/", "D:\\absolute\\path\\to\\file", false},
+			{".", pwd, pwd, false},
+			{".", "", pwd, false},
+			{"somefile", "somedir", filepath.Join(pwd, "somedir", "somefile"), false},
+		}
+
+		for _, test := range tests {
+			result, err := expandPathImpl(test.path, test.relativeDir, mockCurrentUser, mockLookupUser)
+			if (err != nil) != test.shouldErr {
+				t.Errorf("expandPathImpl(%q) returned error: %v, expected error: %v", test.path, err != nil, test.shouldErr)
+			}
+
+			if result != test.expected && !test.shouldErr {
+				t.Errorf("expandPathImpl(%q) = %q, want %q", test.path, result, test.expected)
+			}
+		}
+	})
+}
--- a/parser/parser.go
+++ b/parser/parser.go
@@ -620,43 +620,43 @@ func isValidCommand(cmd string) bool {
 	}
 }

-func expandPath(path, dir string) (string, error) {
-	if filepath.IsAbs(path) {
-		return path, nil
-	}
+func expandPathImpl(path, relativeDir string, currentUserFunc func() (*user.User, error), lookupUserFunc func(string) (*user.User, error)) (string, error) {
+	if filepath.IsAbs(path) || strings.HasPrefix(path, "\\") || strings.HasPrefix(path, "/") {
+		return filepath.Abs(path)
+	} else if strings.HasPrefix(path, "~") {
+		var homeDir string

-	path, found := strings.CutPrefix(path, "~")
-	switch {
-	case !found:
-		// make path relative to dir
-		if !filepath.IsAbs(dir) {
-			// if dir is relative, make it absolute relative to cwd
-			cwd, err := os.Getwd()
+		if path == "~" || strings.HasPrefix(path, "~/") {
+			// Current user's home directory
+			currentUser, err := currentUserFunc()
 			if err != nil {
-				return "", err
+				return "", fmt.Errorf("failed to get current user: %w", err)
+			}
+			homeDir = currentUser.HomeDir
+			path = strings.TrimPrefix(path, "~")
+		} else {
+			// Specific user's home directory
+			parts := strings.SplitN(path[1:], "/", 2)
+			userInfo, err := lookupUserFunc(parts[0])
+			if err != nil {
+				return "", fmt.Errorf("failed to find user '%s': %w", parts[0], err)
+			}
+			homeDir = userInfo.HomeDir
+			if len(parts) > 1 {
+				path = "/" + parts[1]
+			} else {
+				path = ""
 			}
-			dir = filepath.Join(cwd, dir)
 		}
-		path = filepath.Join(dir, path)
-	case filepath.IsLocal(path):
-		// ~<user>/...
-		// make path relative to specified user's home
-		split := strings.SplitN(path, string(os.PathSeparator), 2)
-		u, err := user.Lookup(split[0])
-		if err != nil {
-			return "", err
-		}
-		split[0] = u.HomeDir
-		path = filepath.Join(split...)
-	default:
-		// ~ or ~/...
-		// make path relative to current user's home
-		home, err := os.UserHomeDir()
-		if err != nil {
-			return "", err
-		}
-		path = filepath.Join(home, path)
+
+		path = filepath.Join(homeDir, path)
+	} else {
+		path = filepath.Join(relativeDir, path)
 	}

-	return filepath.Clean(path), nil
+	return filepath.Abs(path)
+}
+
+func expandPath(path, relativeDir string) (string, error) {
+	return expandPathImpl(path, relativeDir, user.Current, user.Lookup)
 }
--- a/parser/parser_test.go
+++ b/parser/parser_test.go
@@ -9,9 +9,7 @@ import (
 	"io"
 	"maps"
 	"os"
-	"os/user"
 	"path/filepath"
-	"runtime"
 	"strings"
 	"testing"
 	"unicode/utf16"
@@ -1128,62 +1126,3 @@ func TestFilesForModel(t *testing.T) {
 		})
 	}
 }
-
-func TestExpandPath(t *testing.T) {
-	home := t.TempDir()
-	t.Setenv("HOME", home)
-	t.Setenv("USERPROFILE", home)
-
-	cwd, err := os.Getwd()
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	u, err := user.Current()
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	volume := ""
-	if runtime.GOOS == "windows" {
-		volume = "D:"
-	}
-
-	cases := []struct {
-		input,
-		dir,
-		want string
-		err error
-	}{
-		{"~", "", home, nil},
-		{"~/path/to/file", "", filepath.Join(home, filepath.ToSlash("path/to/file")), nil},
-		{"~" + u.Username + "/path/to/file", "", filepath.Join(u.HomeDir, filepath.ToSlash("path/to/file")), nil},
-		{"~nonexistentuser/path/to/file", "", "", user.UnknownUserError("nonexistentuser")},
-		{"relative/path/to/file", "", filepath.Join(cwd, filepath.ToSlash("relative/path/to/file")), nil},
-		{volume + "/absolute/path/to/file", "", filepath.ToSlash(volume + "/absolute/path/to/file"), nil},
-		{volume + "/absolute/path/to/file", filepath.ToSlash("another/path"), filepath.ToSlash(volume + "/absolute/path/to/file"), nil},
-		{".", cwd, cwd, nil},
-		{".", "", cwd, nil},
-		{"", cwd, cwd, nil},
-		{"", "", cwd, nil},
-		{"file", "path/to", filepath.Join(cwd, filepath.ToSlash("path/to/file")), nil},
-	}
-
-	for _, tt := range cases {
-		t.Run(tt.input, func(t *testing.T) {
-			got, err := expandPath(tt.input, tt.dir)
-			// On Windows, user.Lookup does not map syscall errors to user.UnknownUserError
-			// so we special case the test to just check for an error.
-			// See https://cs.opensource.google/go/go/+/refs/tags/go1.25.1:src/os/user/lookup_windows.go;l=455
-			if runtime.GOOS != "windows" && !errors.Is(err, tt.err) {
-				t.Fatalf("expandPath(%q) error = %v, wantErr %v", tt.input, err, tt.err)
-			} else if tt.err != nil && err == nil {
-				t.Fatal("test case expected to fail on windows")
-			}
-
-			if got != tt.want {
-				t.Errorf("expandPath(%q) = %v, want %v", tt.input, got, tt.want)
-			}
-		})
-	}
-}
--- a/server/routes.go
+++ b/server/routes.go
@@ -340,7 +340,7 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 		builtinParser = parsers.ParserForName(m.Config.Parser)
 		if builtinParser != nil {
 			// no tools or last message for generate endpoint
-			builtinParser.Init(nil, nil)
+			builtinParser.Init(nil, nil, req.Think)
 		}
 	}

@@ -2051,7 +2051,7 @@ func (s *Server) ChatHandler(c *gin.Context) {
 				lastMessage = &msgs[len(msgs)-1]
 			}
 			// Initialize parser and get processed tools
-			processedTools = builtinParser.Init(req.Tools, lastMessage)
+			processedTools = builtinParser.Init(req.Tools, lastMessage, req.Think)
 		}
 	}
Author	SHA1	Message	Date
ParthSareen	92af238208	wip	2025-12-02 12:17:36 -08:00
ParthSareen	7461faf651	script to render templates	2025-12-01 18:03:04 -08:00
Daniel Hiltgen	554172759c	win: warn if ggml-base detected in PATH (#13289 ) If the user has somehow installed another GGML based app which places a ggml-base lib somewhere in their PATH, we can experience runtime problems due to incompatibilities. This change adds a warning message if we detect a ggml-base outside of our install location to aid in troubleshooting.	2025-12-01 15:36:47 -08:00
Bruce MacDonald	5b6a8e6001	api/client: handle non-json streaming errors (#13007 ) While processing the response stream during a chat or generation if an error is occurred it is parsed and returned to the user. The issue with the existing code is that this assumed the response would be valid JSON, which is not a safe assumption and caused cryptic error messages to be displayed due to parsing failures: `invalid character 'i' looking for beginning of value` This change updates the stream function to return the raw error string if it cant be parsed as JSON. This should help with debugging issues by making sure the actual error reaches the user.	2025-12-01 15:10:16 -08:00
Daniel Hiltgen	467bbc0dd5	jetpack: require exact match or skip cuda_jetpack* (#13288 ) The cuda_jetpack libs will enumerate discrete GPUs on SBSA systems which leads to runtime failures of missing kernels. This fix requires an exact match to enable jetpacks instead of relying on enumeration to filter out supported libraries.	2025-12-01 12:48:16 -08:00
Jeffrey Morgan	6d9f9323c5	.gitattributes: add app/webview to linguist-vendored (#13274 )	2025-11-29 23:46:10 -05:00
Ondrej Kokes	0c2489605d	docs: fix output formatting in faq.mdx (#13231 ) There were a few Markdown typos in one FAQ answer. It now renders as a proper ascii table.	2025-11-28 19:19:21 -05:00
EntropyYue	8b1b89a984	docs: remove deprecated parameters (#13237 )	2025-11-26 11:03:09 +09:00
Eva H	47e272c35a	app/cmd: update ollama help to navigate to ollama doc instead of github page (#13174 )	2025-11-20 16:30:35 -05:00
Jeffrey Morgan	417a81fda3	app: open app instead of always navigating to / on connect (#13164 )	2025-11-20 12:59:17 -08:00
Daniel Hiltgen	dba62ff3a5	discovery: fix cuda overlap case (#13176 ) Recent refactoring introduced a regression for filtering cuda overlap to favor newest supported version.	2025-11-20 12:15:37 -08:00
Grace	d70e935526	Parser for Cogito v2 (#13145 )	2025-11-19 17:21:07 -08:00
Michael Yang	5c1063df7f	deepseek2: upgrade to run v3+ models (#13166 ) the check for mla omits v3 and r1 which should not return unsupported. instead check the tokenizer for compatibility	2025-11-19 17:05:39 -08:00
Jesse Gross	cb485b2019	kvcache: Run tests both with and without PermutedV The causal cache can store data differently depending on what is best for the backend. We should run tests both ways.	2025-11-19 16:45:30 -08:00
nicole pardal	b2af50960f	nomic-embed: nomic-embed-text defaulted to ollama runner (#13144 )	2025-11-19 13:03:44 -08:00
Michael Yang	eac5b8bfbd	chore: mark vulkan shaders as vendored files	2025-11-19 12:01:23 -08:00
Patrick Devine	604e43b28d	models: enable deepseek2 (deepseek v3.1 w/ MLA) on the new engine (#13151 )	2025-11-18 22:03:50 -08:00
Jesse Gross	53985b3c4d	kvcache: Use SetRows to store cache data We currently copy data into the KV cache in contiguous buffers using ggml_cpy(). ggml_set_rows() was introduced to allow scatter operation so that contiguous buffers are no longer required. The direct primary benefit of this is that we no longer need to perform defragmentation. However, GGML recently removed an optimization for ggml_cpy() and we picked it up in `544b673` "ggml update to b6840 (#12791)". This caused a roughly 40% drop in token generation performance on CUDA due to CUDA graphs no longer being used. By switching to ggml_set_rows(), the original optimization is no longer necessary and CUDA performance is restored. Fixes #13112	2025-11-18 20:42:28 -08:00
Jesse Gross	b6e02cbbd2	ggml: Automatically make tensors contiguous on reshape GGML requires tensors to be contiguous for reshape and if this is not the case, it will assert fail. Contiguous is an expensive operation, so it's best to do it lazily when it is actually required rather than ahead of time when it may not be needed.	2025-11-18 20:42:28 -08:00
Grace	91935631ac	Renderer for Cogito v2 (#13139 )	2025-11-18 19:06:34 -08:00
nicole pardal	8de30b568a	nomic-embed-text model implementation (#13071 )	2025-11-18 18:28:10 -08:00
Daniel Hiltgen	485da9fd35	win: exit instead of abort (#13138 ) Calling abort on windows triggers the C++ runtime to attempt a debugger attach, which causes the crashed runners to hang instead of exit, leading to a timeout instead of a fast failure during discovery.	2025-11-18 16:33:33 -08:00
Michael Yang	0796d79d19	cuda: skip large batches cuda panics on batches larger than 1024 so skip those and fallback to cpu	2025-11-18 16:11:37 -08:00
Michael Yang	92981ae3f2	deepseekocr	2025-11-18 16:11:37 -08:00