* TEMPORARY: Update the llama.cpp upstream to my fork's Granite Four branch
This will be redone once my branch is merged upstream in llama.cpp
* feat: Update all patches
There are a number that are no longer needed at all:
- 0003-embeddings: Embeddings entirely overhauled on master
- 0008-ensure-KV-cache-is-fully-defragmented: KV caching entirely
overhauled on master
- 0019-metal-add-mean-kernel-14267: Merged upstream
- 0020-CUDA-add-mean-operation-14313: Merged upstream
* feat: Sync llama.cpp and ggml
* fix: Update rsync-filter for all moved/new/removed files
* fix: Add files missing from sync
* fix: Update ggml rsync-filter for new ggml-cpu/arch subdirs
* fix: Add ggml files missing from sync
* fix: Narrow llama.cpp rsync-filter to not include mtmd main tool cpp files
* fix: Remove mtmd main cpp files
* fix: Add missing include in sampling_ext.cpp
* fix: Update llama.go to use mtmd instead of clip/llava
* fix: Add patch for mtmd_input_text
* chore: Ignore *.patched in the patch directory
* fix: Fix support for arch-specific ggml-cpu source files with new arrangement
In https://github.com/ggml-org/llama.cpp/pull/13892, all arch-specific
implementations were split out into a nested tree structure under
ggml-cpu/arch. This conflicts with standard CGO layout where all
arch-specific source files are expected to live in the same directory as
the parent go module and use suffixes based on GOOS and GOARCH. As such,
there were really two options for getting this to work:
1. Add a patch on top of the GGML sync to rearrange the files to match the
GO layout convention
2. Use CGO directives to conditionally include the nested source files in
the compilation units
This commit does (2) in order to minimize the set of changes needed on top
of the upstream file layout. To get this to work, there are two key things
needed:
1. In cpu.go, #cgo directives are added to explicitly set __${GOARCH}__ in
the preprocessor directives
2. In arch-impls.c|cpp, use an #ifdef | #elif defined | #endif chain to
explicitly include the .c|.cpp files for the given architecture from the
nested directory
* fix: Use mtmd_helper to correctly load the bitmap for the image
* fix: Apply patch for mtmd_text_input
* fix: Add missing stb to llama.cpp rsync-filter
* fix: Add sync'ed stb vendored header
* fix: Use c++17 and include vendor for go wrapper modules
* fix: Update patch 0015 for upstream implementation of uuid
* feat: Bump to the latest tip of the branch
* fix: Update patches for bump
* feat: Bump back to the cenral repo and point at the latest master
This includes granite 4 and a number of other model architectures!
* fix: Revert changes to ggml export GPU UUID patch
* fix: Add patch for GGML_VERSION and GGML_COMMIT constants
* feat: Sync all patched code
* build: Include cmake/common.cmake in ggml sync
* build: Add top-level include for GNUINstallDirs in CMakeLists.txt
This is used to populate CMAKE_INSTALL_BINDIR
* fix: Add a patch to avoid power throttling API on non-msvc windows builds
* fix: Sync patch changes for ggml-cpu.c
* feat: Bump llama.cpp to 4a4f42
This picks up support for Kimi K2 and PLaMO-2
* feat: Sync llama.cpp
* fix: Handle multi-chunk image encodings from mtmd
* fix: Re-number patches after merge with `main`
* feat: Bump to 41e78c in the makefile
* fix: Fix Solar and argsort/copy patches after bump
* fix: Remove Gemma3n CUDA Graphs patch
It was implemented upstream:
https://github.com/ggml-org/llama.cpp/pull/14741
* feat: Sync llama.cpp / ggml after latest bump
* build: Remove unnecessary CFLAGS definitions in cpu.go
* fix: Remove unnecessary additions in the rsync-filter
* fix: Remove unused vendored code for chat template parsing
* Revert "fix: Remove Gemma3n CUDA Graphs patch"
This reverts commit d724caced3.
* fix: Update 0020 CUDA Graphs for gemma3n to keep both llama.cpp and ollama fixes
https://github.com/ollama/ollama/pull/11195#issuecomment-3137312394
* fix: Sync ggml-cuda.cu after keeping both style cuda graph fixes for gemma3n
* unwind mxfp4 patch
Prepare to bump ggml with their impl for mxfp4
* bump
* fix windows build error
* Convert tensors at load time
Repack the mxfp4 tensors as ggmls kernels expect them to be.
* convert mlp bf16 to f32
* buffer the conversion better
* reshape earlier
* openai swiglu
* add ids
* split qkv, gate_up
* fix nested alt tags
* fast attention
* remove debug messages
* fix lint
* remove redundant test
* remap values only if source/target are different
* add back i32->i32 copy
* refactor cpu quants
* clean up vendor
* update patch instructions
* clean up patches
* remove webgpu
* update mem
* also handle gpt-oss
* revert convert changes
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
773 lines
33 KiB
C++
Vendored
773 lines
33 KiB
C++
Vendored
#include "llama-chat.h"
|
||
|
||
#include "llama.h"
|
||
|
||
#include <map>
|
||
#include <sstream>
|
||
#include <algorithm>
|
||
|
||
#if __cplusplus >= 202000L
|
||
#define LU8(x) (const char*)(u8##x)
|
||
#else
|
||
#define LU8(x) u8##x
|
||
#endif
|
||
|
||
// trim whitespace from the beginning and end of a string
|
||
static std::string trim(const std::string & str) {
|
||
size_t start = 0;
|
||
size_t end = str.size();
|
||
while (start < end && isspace(str[start])) {
|
||
start += 1;
|
||
}
|
||
while (end > start && isspace(str[end - 1])) {
|
||
end -= 1;
|
||
}
|
||
return str.substr(start, end - start);
|
||
}
|
||
|
||
static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
|
||
{ "chatml", LLM_CHAT_TEMPLATE_CHATML },
|
||
{ "llama2", LLM_CHAT_TEMPLATE_LLAMA_2 },
|
||
{ "llama2-sys", LLM_CHAT_TEMPLATE_LLAMA_2_SYS },
|
||
{ "llama2-sys-bos", LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS },
|
||
{ "llama2-sys-strip", LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP },
|
||
{ "mistral-v1", LLM_CHAT_TEMPLATE_MISTRAL_V1 },
|
||
{ "mistral-v3", LLM_CHAT_TEMPLATE_MISTRAL_V3 },
|
||
{ "mistral-v3-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN },
|
||
{ "mistral-v7", LLM_CHAT_TEMPLATE_MISTRAL_V7 },
|
||
{ "mistral-v7-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V7_TEKKEN },
|
||
{ "phi3", LLM_CHAT_TEMPLATE_PHI_3 },
|
||
{ "phi4", LLM_CHAT_TEMPLATE_PHI_4 },
|
||
{ "falcon3", LLM_CHAT_TEMPLATE_FALCON_3 },
|
||
{ "zephyr", LLM_CHAT_TEMPLATE_ZEPHYR },
|
||
{ "monarch", LLM_CHAT_TEMPLATE_MONARCH },
|
||
{ "gemma", LLM_CHAT_TEMPLATE_GEMMA },
|
||
{ "orion", LLM_CHAT_TEMPLATE_ORION },
|
||
{ "openchat", LLM_CHAT_TEMPLATE_OPENCHAT },
|
||
{ "vicuna", LLM_CHAT_TEMPLATE_VICUNA },
|
||
{ "vicuna-orca", LLM_CHAT_TEMPLATE_VICUNA_ORCA },
|
||
{ "deepseek", LLM_CHAT_TEMPLATE_DEEPSEEK },
|
||
{ "deepseek2", LLM_CHAT_TEMPLATE_DEEPSEEK_2 },
|
||
{ "deepseek3", LLM_CHAT_TEMPLATE_DEEPSEEK_3 },
|
||
{ "command-r", LLM_CHAT_TEMPLATE_COMMAND_R },
|
||
{ "llama3", LLM_CHAT_TEMPLATE_LLAMA_3 },
|
||
{ "chatglm3", LLM_CHAT_TEMPLATE_CHATGLM_3 },
|
||
{ "chatglm4", LLM_CHAT_TEMPLATE_CHATGLM_4 },
|
||
{ "glmedge", LLM_CHAT_TEMPLATE_GLMEDGE },
|
||
{ "minicpm", LLM_CHAT_TEMPLATE_MINICPM },
|
||
{ "exaone3", LLM_CHAT_TEMPLATE_EXAONE_3 },
|
||
{ "exaone4", LLM_CHAT_TEMPLATE_EXAONE_4 },
|
||
{ "rwkv-world", LLM_CHAT_TEMPLATE_RWKV_WORLD },
|
||
{ "granite", LLM_CHAT_TEMPLATE_GRANITE },
|
||
{ "gigachat", LLM_CHAT_TEMPLATE_GIGACHAT },
|
||
{ "megrez", LLM_CHAT_TEMPLATE_MEGREZ },
|
||
{ "yandex", LLM_CHAT_TEMPLATE_YANDEX },
|
||
{ "bailing", LLM_CHAT_TEMPLATE_BAILING },
|
||
{ "llama4", LLM_CHAT_TEMPLATE_LLAMA4 },
|
||
{ "smolvlm", LLM_CHAT_TEMPLATE_SMOLVLM },
|
||
{ "hunyuan-moe", LLM_CHAT_TEMPLATE_HUNYUAN_MOE },
|
||
{ "gpt-oss", LLM_CHAT_TEMPLATE_OPENAI_MOE },
|
||
{ "hunyuan-dense", LLM_CHAT_TEMPLATE_HUNYUAN_DENSE },
|
||
{ "kimi-k2", LLM_CHAT_TEMPLATE_KIMI_K2 },
|
||
};
|
||
|
||
llm_chat_template llm_chat_template_from_str(const std::string & name) {
|
||
return LLM_CHAT_TEMPLATES.at(name);
|
||
}
|
||
|
||
llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
|
||
try {
|
||
return llm_chat_template_from_str(tmpl);
|
||
} catch (const std::out_of_range &) {
|
||
// ignore
|
||
}
|
||
|
||
auto tmpl_contains = [&tmpl](const char * haystack) -> bool {
|
||
return tmpl.find(haystack) != std::string::npos;
|
||
};
|
||
if (tmpl_contains("<|im_start|>")) {
|
||
return tmpl_contains("<|im_sep|>")
|
||
? LLM_CHAT_TEMPLATE_PHI_4
|
||
: tmpl_contains("<end_of_utterance>")
|
||
? LLM_CHAT_TEMPLATE_SMOLVLM // SmolVLM uses <|im_start|> as BOS, but it is NOT chatml
|
||
: LLM_CHAT_TEMPLATE_CHATML;
|
||
} else if (tmpl.find("mistral") == 0 || tmpl_contains("[INST]")) {
|
||
if (tmpl_contains("[SYSTEM_PROMPT]")) {
|
||
return LLM_CHAT_TEMPLATE_MISTRAL_V7;
|
||
} else if (
|
||
// catches official 'v1' template
|
||
tmpl_contains("' [INST] ' + system_message")
|
||
// catches official 'v3' and 'v3-tekken' templates
|
||
|| tmpl_contains("[AVAILABLE_TOOLS]")
|
||
) {
|
||
// Official mistral 'v1', 'v3' and 'v3-tekken' templates
|
||
// See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/chat_templates.md
|
||
// See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/templates.md
|
||
if (tmpl_contains(" [INST]")) {
|
||
return LLM_CHAT_TEMPLATE_MISTRAL_V1;
|
||
} else if (tmpl_contains("\"[INST]\"")) {
|
||
return LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN;
|
||
}
|
||
return LLM_CHAT_TEMPLATE_MISTRAL_V3;
|
||
} else {
|
||
// llama2 template and its variants
|
||
// [variant] support system message
|
||
// See: https://huggingface.co/blog/llama2#how-to-prompt-llama-2
|
||
bool support_system_message = tmpl_contains("<<SYS>>");
|
||
bool add_bos_inside_history = tmpl_contains("bos_token + '[INST]");
|
||
bool strip_message = tmpl_contains("content.strip()");
|
||
if (strip_message) {
|
||
return LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP;
|
||
} else if (add_bos_inside_history) {
|
||
return LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS;
|
||
} else if (support_system_message) {
|
||
return LLM_CHAT_TEMPLATE_LLAMA_2_SYS;
|
||
} else {
|
||
return LLM_CHAT_TEMPLATE_LLAMA_2;
|
||
}
|
||
}
|
||
} else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>")) {
|
||
return LLM_CHAT_TEMPLATE_PHI_3;
|
||
} else if (tmpl_contains("[gMASK]<sop>")) {
|
||
return LLM_CHAT_TEMPLATE_CHATGLM_4;
|
||
} else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|user|>")) {
|
||
return tmpl_contains("</s>") ? LLM_CHAT_TEMPLATE_FALCON_3 : LLM_CHAT_TEMPLATE_GLMEDGE;
|
||
} else if (tmpl_contains("<|{{ item['role'] }}|>") && tmpl_contains("<|begin_of_image|>")) {
|
||
return LLM_CHAT_TEMPLATE_GLMEDGE;
|
||
} else if (tmpl_contains("<|user|>") && tmpl_contains("<|endoftext|>")) {
|
||
return LLM_CHAT_TEMPLATE_ZEPHYR;
|
||
} else if (tmpl_contains("bos_token + message['role']")) {
|
||
return LLM_CHAT_TEMPLATE_MONARCH;
|
||
} else if (tmpl_contains("<start_of_turn>")) {
|
||
return LLM_CHAT_TEMPLATE_GEMMA;
|
||
} else if (tmpl_contains("'\\n\\nAssistant: ' + eos_token")) {
|
||
// OrionStarAI/Orion-14B-Chat
|
||
return LLM_CHAT_TEMPLATE_ORION;
|
||
} else if (tmpl_contains("GPT4 Correct ")) {
|
||
// openchat/openchat-3.5-0106
|
||
return LLM_CHAT_TEMPLATE_OPENCHAT;
|
||
} else if (tmpl_contains("USER: ") && tmpl_contains("ASSISTANT: ")) {
|
||
// eachadea/vicuna-13b-1.1 (and Orca variant)
|
||
if (tmpl_contains("SYSTEM: ")) {
|
||
return LLM_CHAT_TEMPLATE_VICUNA_ORCA;
|
||
}
|
||
return LLM_CHAT_TEMPLATE_VICUNA;
|
||
} else if (tmpl_contains("### Instruction:") && tmpl_contains("<|EOT|>")) {
|
||
// deepseek-ai/deepseek-coder-33b-instruct
|
||
return LLM_CHAT_TEMPLATE_DEEPSEEK;
|
||
} else if (tmpl_contains("<|START_OF_TURN_TOKEN|>") && tmpl_contains("<|USER_TOKEN|>")) {
|
||
// CohereForAI/c4ai-command-r-plus
|
||
return LLM_CHAT_TEMPLATE_COMMAND_R;
|
||
} else if (tmpl_contains("<|start_header_id|>") && tmpl_contains("<|end_header_id|>")) {
|
||
return LLM_CHAT_TEMPLATE_LLAMA_3;
|
||
} else if (tmpl_contains("[gMASK]sop")) {
|
||
// chatglm3-6b
|
||
return LLM_CHAT_TEMPLATE_CHATGLM_3;
|
||
} else if (tmpl_contains(LU8("<用户>"))) {
|
||
// MiniCPM-3B-OpenHermes-2.5-v2-GGUF
|
||
return LLM_CHAT_TEMPLATE_MINICPM;
|
||
} else if (tmpl_contains("'Assistant: ' + message['content'] + eos_token")) {
|
||
return LLM_CHAT_TEMPLATE_DEEPSEEK_2;
|
||
} else if (tmpl_contains(LU8("<|Assistant|>")) && tmpl_contains(LU8("<|User|>")) && tmpl_contains(LU8("<|end▁of▁sentence|>"))) {
|
||
return LLM_CHAT_TEMPLATE_DEEPSEEK_3;
|
||
} else if (tmpl_contains("[|system|]") && tmpl_contains("[|assistant|]") && tmpl_contains("[|endofturn|]")) {
|
||
if (tmpl_contains("[|tool|]")) {
|
||
return LLM_CHAT_TEMPLATE_EXAONE_4;
|
||
}
|
||
// ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb
|
||
// EXAONE-3.0-7.8B-Instruct
|
||
return LLM_CHAT_TEMPLATE_EXAONE_3;
|
||
} else if (tmpl_contains("rwkv-world") || tmpl_contains("{{- 'User: ' + message['content']|trim + '\\n\\n' -}}")) {
|
||
return LLM_CHAT_TEMPLATE_RWKV_WORLD;
|
||
} else if (tmpl_contains("<|start_of_role|>")) {
|
||
return LLM_CHAT_TEMPLATE_GRANITE;
|
||
} else if (tmpl_contains("message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1]")) {
|
||
return LLM_CHAT_TEMPLATE_GIGACHAT;
|
||
} else if (tmpl_contains("<|role_start|>")) {
|
||
return LLM_CHAT_TEMPLATE_MEGREZ;
|
||
} else if (tmpl_contains(" Ассистент:")) {
|
||
return LLM_CHAT_TEMPLATE_YANDEX;
|
||
} else if (tmpl_contains("<role>ASSISTANT</role>") && tmpl_contains("'HUMAN'")) {
|
||
return LLM_CHAT_TEMPLATE_BAILING;
|
||
} else if (tmpl_contains("<|header_start|>") && tmpl_contains("<|header_end|>")) {
|
||
return LLM_CHAT_TEMPLATE_LLAMA4;
|
||
} else if (tmpl_contains("<|endofuserprompt|>")) {
|
||
return LLM_CHAT_TEMPLATE_DOTS1;
|
||
} else if (tmpl_contains("<|extra_0|>") && tmpl_contains("<|extra_4|>")) {
|
||
return LLM_CHAT_TEMPLATE_HUNYUAN_MOE;
|
||
} else if (tmpl_contains("<|start|>") && tmpl_contains("<|channel|>")) {
|
||
return LLM_CHAT_TEMPLATE_OPENAI_MOE;
|
||
} else if (tmpl_contains("<|hy_Assistant|>") && tmpl_contains("<|hy_place▁holder▁no▁3|>")) {
|
||
return LLM_CHAT_TEMPLATE_HUNYUAN_DENSE;
|
||
} else if (tmpl_contains("<|im_assistant|>assistant<|im_middle|>")) {
|
||
return LLM_CHAT_TEMPLATE_KIMI_K2;
|
||
}
|
||
return LLM_CHAT_TEMPLATE_UNKNOWN;
|
||
}
|
||
|
||
// Simple version of "llama_apply_chat_template" that only works with strings
|
||
// This function uses heuristic checks to determine commonly used template. It is not a jinja parser.
|
||
int32_t llm_chat_apply_template(
|
||
llm_chat_template tmpl,
|
||
const std::vector<const llama_chat_message *> & chat,
|
||
std::string & dest, bool add_ass) {
|
||
// Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527
|
||
std::stringstream ss;
|
||
if (tmpl == LLM_CHAT_TEMPLATE_CHATML) {
|
||
// chatml template
|
||
for (auto message : chat) {
|
||
ss << "<|im_start|>" << message->role << "\n" << message->content << "<|im_end|>\n";
|
||
}
|
||
if (add_ass) {
|
||
ss << "<|im_start|>assistant\n";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V7 || tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V7_TEKKEN) {
|
||
// Official mistral 'v7' template
|
||
// See: https://huggingface.co/mistralai/Mistral-Large-Instruct-2411#basic-instruct-template-v7
|
||
// https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503#basic-instruct-template-v7-tekken
|
||
const char * trailing_space = tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V7 ? " " : "";
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
std::string content(message->content);
|
||
if (role == "system") {
|
||
ss << "[SYSTEM_PROMPT]" << trailing_space << content << "[/SYSTEM_PROMPT]";
|
||
} else if (role == "user") {
|
||
ss << "[INST]" << trailing_space << content << "[/INST]";
|
||
} else {
|
||
ss << trailing_space << content << "</s>";
|
||
}
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V1
|
||
|| tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3
|
||
|| tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN) {
|
||
// See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/chat_templates.md
|
||
// See: https://github.com/mistralai/cookbook/blob/main/concept-deep-dive/tokenization/templates.md
|
||
std::string leading_space = tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V1 ? " " : "";
|
||
std::string trailing_space = tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN ? "" : " ";
|
||
bool trim_assistant_message = tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V3;
|
||
bool is_inside_turn = false;
|
||
for (auto message : chat) {
|
||
if (!is_inside_turn) {
|
||
ss << leading_space << "[INST]" << trailing_space;
|
||
is_inside_turn = true;
|
||
}
|
||
std::string role(message->role);
|
||
std::string content(message->content);
|
||
if (role == "system") {
|
||
ss << content << "\n\n";
|
||
} else if (role == "user") {
|
||
ss << content << leading_space << "[/INST]";
|
||
} else {
|
||
ss << trailing_space << (trim_assistant_message ? trim(content) : content) << "</s>";
|
||
is_inside_turn = false;
|
||
}
|
||
}
|
||
} else if (
|
||
tmpl == LLM_CHAT_TEMPLATE_LLAMA_2
|
||
|| tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS
|
||
|| tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS
|
||
|| tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP) {
|
||
// llama2 template and its variants
|
||
// [variant] support system message
|
||
// See: https://huggingface.co/blog/llama2#how-to-prompt-llama-2
|
||
bool support_system_message = tmpl != LLM_CHAT_TEMPLATE_LLAMA_2;
|
||
// [variant] add BOS inside history
|
||
bool add_bos_inside_history = tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_BOS;
|
||
// [variant] trim spaces from the input message
|
||
bool strip_message = tmpl == LLM_CHAT_TEMPLATE_LLAMA_2_SYS_STRIP;
|
||
// construct the prompt
|
||
bool is_inside_turn = true; // skip BOS at the beginning
|
||
ss << "[INST] ";
|
||
for (auto message : chat) {
|
||
std::string content = strip_message ? trim(message->content) : message->content;
|
||
std::string role(message->role);
|
||
if (!is_inside_turn) {
|
||
is_inside_turn = true;
|
||
ss << (add_bos_inside_history ? "<s>[INST] " : "[INST] ");
|
||
}
|
||
if (role == "system") {
|
||
if (support_system_message) {
|
||
ss << "<<SYS>>\n" << content << "\n<</SYS>>\n\n";
|
||
} else {
|
||
// if the model does not support system message, we still include it in the first message, but without <<SYS>>
|
||
ss << content << "\n";
|
||
}
|
||
} else if (role == "user") {
|
||
ss << content << " [/INST]";
|
||
} else {
|
||
ss << content << "</s>";
|
||
is_inside_turn = false;
|
||
}
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_PHI_3) {
|
||
// Phi 3
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
ss << "<|" << role << "|>\n" << message->content << "<|end|>\n";
|
||
}
|
||
if (add_ass) {
|
||
ss << "<|assistant|>\n";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_PHI_4) {
|
||
// chatml template
|
||
for (auto message : chat) {
|
||
ss << "<|im_start|>" << message->role << "<|im_sep|>" << message->content << "<|im_end|>";
|
||
}
|
||
if (add_ass) {
|
||
ss << "<|im_start|>assistant<|im_sep|>";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_FALCON_3) {
|
||
// Falcon 3
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
ss << "<|" << role << "|>\n" << message->content << "\n";
|
||
}
|
||
if (add_ass) {
|
||
ss << "<|assistant|>\n";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_ZEPHYR) {
|
||
// zephyr template
|
||
for (auto message : chat) {
|
||
ss << "<|" << message->role << "|>" << "\n" << message->content << "<|endoftext|>\n";
|
||
}
|
||
if (add_ass) {
|
||
ss << "<|assistant|>\n";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_MONARCH) {
|
||
// mlabonne/AlphaMonarch-7B template (the <s> is included inside history)
|
||
for (auto message : chat) {
|
||
std::string bos = (message == chat.front()) ? "" : "<s>"; // skip BOS for first message
|
||
ss << bos << message->role << "\n" << message->content << "</s>\n";
|
||
}
|
||
if (add_ass) {
|
||
ss << "<s>assistant\n";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_GEMMA) {
|
||
// google/gemma-7b-it
|
||
std::string system_prompt = "";
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
if (role == "system") {
|
||
// there is no system message for gemma, but we will merge it with user prompt, so nothing is broken
|
||
system_prompt += trim(message->content);
|
||
continue;
|
||
}
|
||
// in gemma, "assistant" is "model"
|
||
role = role == "assistant" ? "model" : message->role;
|
||
ss << "<start_of_turn>" << role << "\n";
|
||
if (!system_prompt.empty() && role != "model") {
|
||
ss << system_prompt << "\n\n";
|
||
system_prompt = "";
|
||
}
|
||
ss << trim(message->content) << "<end_of_turn>\n";
|
||
}
|
||
if (add_ass) {
|
||
ss << "<start_of_turn>model\n";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_ORION) {
|
||
// OrionStarAI/Orion-14B-Chat
|
||
std::string system_prompt = "";
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
if (role == "system") {
|
||
// there is no system message support, we will merge it with user prompt
|
||
system_prompt += message->content;
|
||
continue;
|
||
} else if (role == "user") {
|
||
ss << "Human: ";
|
||
if (!system_prompt.empty()) {
|
||
ss << system_prompt << "\n\n";
|
||
system_prompt = "";
|
||
}
|
||
ss << message->content << "\n\nAssistant: </s>";
|
||
} else {
|
||
ss << message->content << "</s>";
|
||
}
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_OPENCHAT) {
|
||
// openchat/openchat-3.5-0106,
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
if (role == "system") {
|
||
ss << message->content << "<|end_of_turn|>";
|
||
} else {
|
||
role[0] = toupper(role[0]);
|
||
ss << "GPT4 Correct " << role << ": " << message->content << "<|end_of_turn|>";
|
||
}
|
||
}
|
||
if (add_ass) {
|
||
ss << "GPT4 Correct Assistant:";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_VICUNA || tmpl == LLM_CHAT_TEMPLATE_VICUNA_ORCA) {
|
||
// eachadea/vicuna-13b-1.1 (and Orca variant)
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
if (role == "system") {
|
||
// Orca-Vicuna variant uses a system prefix
|
||
if (tmpl == LLM_CHAT_TEMPLATE_VICUNA_ORCA) {
|
||
ss << "SYSTEM: " << message->content << "\n";
|
||
} else {
|
||
ss << message->content << "\n\n";
|
||
}
|
||
} else if (role == "user") {
|
||
ss << "USER: " << message->content << "\n";
|
||
} else if (role == "assistant") {
|
||
ss << "ASSISTANT: " << message->content << "</s>\n";
|
||
}
|
||
}
|
||
if (add_ass) {
|
||
ss << "ASSISTANT:";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_DEEPSEEK) {
|
||
// deepseek-ai/deepseek-coder-33b-instruct
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
if (role == "system") {
|
||
ss << message->content;
|
||
} else if (role == "user") {
|
||
ss << "### Instruction:\n" << message->content << "\n";
|
||
} else if (role == "assistant") {
|
||
ss << "### Response:\n" << message->content << "\n<|EOT|>\n";
|
||
}
|
||
}
|
||
if (add_ass) {
|
||
ss << "### Response:\n";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_COMMAND_R) {
|
||
// CohereForAI/c4ai-command-r-plus
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
if (role == "system") {
|
||
ss << "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>";
|
||
} else if (role == "user") {
|
||
ss << "<|START_OF_TURN_TOKEN|><|USER_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>";
|
||
} else if (role == "assistant") {
|
||
ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>";
|
||
}
|
||
}
|
||
if (add_ass) {
|
||
ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_LLAMA_3) {
|
||
// Llama 3
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
ss << "<|start_header_id|>" << role << "<|end_header_id|>\n\n" << trim(message->content) << "<|eot_id|>";
|
||
}
|
||
if (add_ass) {
|
||
ss << "<|start_header_id|>assistant<|end_header_id|>\n\n";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_CHATGLM_3) {
|
||
// chatglm3-6b
|
||
ss << "[gMASK]" << "sop";
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
ss << "<|" << role << "|>" << "\n " << message->content;
|
||
}
|
||
if (add_ass) {
|
||
ss << "<|assistant|>";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_CHATGLM_4) {
|
||
ss << "[gMASK]" << "<sop>";
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
ss << "<|" << role << "|>" << "\n" << message->content;
|
||
}
|
||
if (add_ass) {
|
||
ss << "<|assistant|>\n";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_GLMEDGE) {
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
ss << "<|" << role << "|>" << "\n" << message->content;
|
||
}
|
||
if (add_ass) {
|
||
ss << "<|assistant|>";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_MINICPM) {
|
||
// MiniCPM-3B-OpenHermes-2.5-v2-GGUF
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
if (role == "user") {
|
||
ss << LU8("<用户>");
|
||
ss << trim(message->content);
|
||
ss << "<AI>";
|
||
} else {
|
||
ss << trim(message->content);
|
||
}
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_DEEPSEEK_2) {
|
||
// DeepSeek-V2
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
if (role == "system") {
|
||
ss << message->content << "\n\n";
|
||
} else if (role == "user") {
|
||
ss << "User: " << message->content << "\n\n";
|
||
} else if (role == "assistant") {
|
||
ss << "Assistant: " << message->content << LU8("<|end▁of▁sentence|>");
|
||
}
|
||
}
|
||
if (add_ass) {
|
||
ss << "Assistant:";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_DEEPSEEK_3) {
|
||
// DeepSeek-V3
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
if (role == "system") {
|
||
ss << message->content << "\n\n";
|
||
} else if (role == "user") {
|
||
ss << LU8("<|User|>") << message->content;
|
||
} else if (role == "assistant") {
|
||
ss << LU8("<|Assistant|>") << message->content << LU8("<|end▁of▁sentence|>");
|
||
}
|
||
}
|
||
if (add_ass) {
|
||
ss << LU8("<|Assistant|>");
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_EXAONE_3) {
|
||
// ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb
|
||
// EXAONE-3.0-7.8B-Instruct
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
if (role == "system") {
|
||
ss << "[|system|]" << trim(message->content) << "[|endofturn|]\n";
|
||
} else if (role == "user") {
|
||
ss << "[|user|]" << trim(message->content) << "\n";
|
||
} else if (role == "assistant") {
|
||
ss << "[|assistant|]" << trim(message->content) << "[|endofturn|]\n";
|
||
}
|
||
}
|
||
if (add_ass) {
|
||
ss << "[|assistant|]";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_EXAONE_4) {
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
if (role == "system") {
|
||
ss << "[|system|]" << trim(message->content) << "[|endofturn|]\n";
|
||
} else if (role == "user") {
|
||
ss << "[|user|]" << trim(message->content) << "\n";
|
||
} else if (role == "assistant") {
|
||
ss << "[|assistant|]" << trim(message->content) << "[|endofturn|]\n";
|
||
} else if (role == "tool") {
|
||
ss << "[|tool|]" << trim(message->content) << "[|endofturn|]\n";
|
||
}
|
||
}
|
||
if (add_ass) {
|
||
ss << "[|assistant|]";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_RWKV_WORLD) {
|
||
// this template requires the model to have "\n\n" as EOT token
|
||
for (size_t i = 0; i < chat.size(); i++) {
|
||
std::string role(chat[i]->role);
|
||
if (role == "system") {
|
||
ss << "System: " << trim(chat[i]->content) << "\n\n";
|
||
} else if (role == "user") {
|
||
ss << "User: " << trim(chat[i]->content) << "\n\n";
|
||
if (i == chat.size() - 1) {
|
||
ss << "Assistant:";
|
||
}
|
||
} else if (role == "assistant") {
|
||
ss << "Assistant: " << trim(chat[i]->content) << "\n\n";
|
||
}
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_GRANITE) {
|
||
// IBM Granite template
|
||
for (const auto & message : chat) {
|
||
std::string role(message->role);
|
||
ss << "<|start_of_role|>" << role << "<|end_of_role|>";
|
||
if (role == "assistant_tool_call") {
|
||
ss << "<|tool_call|>";
|
||
}
|
||
ss << message->content << "<|end_of_text|>\n";
|
||
}
|
||
if (add_ass) {
|
||
ss << "<|start_of_role|>assistant<|end_of_role|>\n";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_GIGACHAT) {
|
||
// GigaChat template
|
||
bool has_system = !chat.empty() && std::string(chat[0]->role) == "system";
|
||
|
||
// Handle system message if present
|
||
if (has_system) {
|
||
ss << "<s>" << chat[0]->content << "<|message_sep|>";
|
||
} else {
|
||
ss << "<s>";
|
||
}
|
||
|
||
// Process remaining messages
|
||
for (size_t i = has_system ? 1 : 0; i < chat.size(); i++) {
|
||
std::string role(chat[i]->role);
|
||
if (role == "user") {
|
||
ss << "user<|role_sep|>" << chat[i]->content << "<|message_sep|>"
|
||
<< "available functions<|role_sep|>[]<|message_sep|>";
|
||
} else if (role == "assistant") {
|
||
ss << "assistant<|role_sep|>" << chat[i]->content << "<|message_sep|>";
|
||
}
|
||
}
|
||
|
||
// Add generation prompt if needed
|
||
if (add_ass) {
|
||
ss << "assistant<|role_sep|>";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_MEGREZ) {
|
||
// Megrez template
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
ss << "<|role_start|>" << role << "<|role_end|>" << message->content << "<|turn_end|>";
|
||
}
|
||
|
||
if (add_ass) {
|
||
ss << "<|role_start|>assistant<|role_end|>";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_YANDEX) {
|
||
// Yandex template ("\n\n" is defined as EOT token)
|
||
|
||
for (size_t i = 0; i < chat.size(); i++) {
|
||
std::string role(chat[i]->role);
|
||
if (role == "user") {
|
||
ss << " Пользователь: " << chat[i]->content << "\n\n";
|
||
} else if (role == "assistant") {
|
||
ss << " Ассистент: " << chat[i]->content << "\n\n";
|
||
}
|
||
}
|
||
|
||
// Add generation prompt if needed
|
||
if (add_ass) {
|
||
ss << " Ассистент:[SEP]";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_BAILING) {
|
||
// Bailing (Ling) template
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
|
||
if (role == "user") {
|
||
role = "HUMAN";
|
||
} else {
|
||
std::transform(role.begin(), role.end(), role.begin(), ::toupper);
|
||
}
|
||
|
||
ss << "<role>" << role << "</role>" << message->content;
|
||
}
|
||
|
||
if (add_ass) {
|
||
ss << "<role>ASSISTANT</role>";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_LLAMA4) {
|
||
// Llama 4
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
ss << "<|header_start|>" << role << "<|header_end|>\n\n" << trim(message->content) << "<|eot|>";
|
||
}
|
||
if (add_ass) {
|
||
ss << "<|header_start|>assistant<|header_end|>\n\n";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_SMOLVLM) {
|
||
// SmolVLM
|
||
ss << "<|im_start|>"; // uses <|im_start|> as BOS, but the actual content is NOT chatml
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
if (role == "system") {
|
||
ss << message->content << "\n\n";
|
||
} else if (role == "user") {
|
||
ss << "User: " << message->content << "<end_of_utterance>\n";
|
||
} else {
|
||
ss << "Assistant: " << message->content << "<end_of_utterance>\n";
|
||
}
|
||
}
|
||
if (add_ass) {
|
||
ss << "Assistant:";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_DOTS1) {
|
||
// dots.llm1.inst (DOTS1)
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
if (role == "system") {
|
||
ss << "<|system|>" << message->content << "<|endofsystem|>";
|
||
} else if (role == "user") {
|
||
ss << "<|userprompt|>" << message->content << "<|endofuserprompt|>";
|
||
} else {
|
||
ss << "<|response|>" << message->content << "<|endofresponse|>";
|
||
}
|
||
}
|
||
if (add_ass) {
|
||
ss << "<|response|>";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_HUNYUAN_MOE) {
|
||
// tencent/Hunyuan-A13B-Instruct
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
if (role == "system") {
|
||
ss << "<|startoftext|>" << message->content << "<|extra_4|>";
|
||
} else if (role == "assistant") {
|
||
ss << message->content << "<|eos|>";
|
||
} else {
|
||
ss << "<|startoftext|>" << message->content << "<|extra_0|>";
|
||
}
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_OPENAI_MOE) {
|
||
// OpenAI MoE (based on Harmony chat template)
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
ss << "<|start|>" << role << "<|message|>" << message->content;
|
||
ss << (role == "assistant" ? "<|return|>" : "<|end|>");
|
||
}
|
||
if (add_ass) {
|
||
ss << "<|start|>assistant";
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_HUNYUAN_DENSE) {
|
||
// tencent/Hunyuan-4B-Instruct
|
||
for (size_t i = 0; i < chat.size(); i++) {
|
||
std::string role(chat[i]->role);
|
||
if (i == 0) {
|
||
if (role == "system") {
|
||
ss << chat[i]->content << "<|hy_place▁holder▁no▁3|>";
|
||
}
|
||
}
|
||
|
||
if (role == "assistant") {
|
||
ss << "<|hy_Assistant|>" << chat[i]->content << "<|hy_place▁holder▁no▁2|>";
|
||
} else if (role == "user") {
|
||
ss << "<|hy_User|>" << chat[i]->content << "<|hy_Assistant|>";
|
||
}
|
||
}
|
||
} else if (tmpl == LLM_CHAT_TEMPLATE_KIMI_K2) {
|
||
// moonshotai/Kimi-K2-Instruct
|
||
for (auto message : chat) {
|
||
std::string role(message->role);
|
||
if (role == "system") {
|
||
ss << "<|im_system|>system<|im_middle|>";
|
||
} else if (role == "user") {
|
||
ss << "<|im_user|>user<|im_middle|>";
|
||
} else if (role == "assistant") {
|
||
ss << "<|im_assistant|>assistant<|im_middle|>";
|
||
} else if (role == "tool") {
|
||
ss << "<|im_system|>tool<|im_middle|>";
|
||
}
|
||
|
||
ss << message->content << "<|im_end|>";
|
||
}
|
||
if (add_ass) {
|
||
ss << "<|im_assistant|>assistant<|im_middle|>";
|
||
}
|
||
} else {
|
||
// template not supported
|
||
return -1;
|
||
}
|
||
dest = ss.str();
|
||
return dest.size();
|
||
}
|
||
|
||
// public interface
|
||
|
||
int32_t llama_chat_builtin_templates(const char ** output, size_t len) {
|
||
auto it = LLM_CHAT_TEMPLATES.begin();
|
||
for (size_t i = 0; i < std::min(len, LLM_CHAT_TEMPLATES.size()); i++) {
|
||
output[i] = it->first.c_str();
|
||
std::advance(it, 1);
|
||
}
|
||
return (int32_t) LLM_CHAT_TEMPLATES.size();
|
||
}
|