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fixed converter

This commit is contained in:
nicole pardal 2025-12-10 16:11:52 -08:00
parent b6f769ae60
commit 29a2d6d931
1 changed files with 54 additions and 24 deletions
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78
convert/convert_olmo.go
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@ -6,54 +6,84 @@ import (
"github.com/ollama/ollama/fs/ggml"
)
type ropeScaling struct {
Factor float32 `json:"factor"`
OriginalMaxPositionEmbeds uint32 `json:"original_max_position_embeddings"`
AttentionFactor float32 `json:"attention_factor"`
BetaFast float32 `json:"beta_fast"`
BetaSlow float32 `json:"beta_slow"`
RopeType string `json:"rope_type"`
ExtrapolationFactor float32 `json:"extrapolation_factor"`
}
type olmoModel struct {
ModelParameters
HiddenSize uint32 `json:"hidden_size"`
NumHiddenLayers uint32 `json:"num_hidden_layers"`
IntermediateSize uint32 `json:"intermediate_size"`
NumAttentionHeads uint32 `json:"num_attention_heads"`
NumKeyValueHeads uint32 `json:"num_key_value_heads"`
MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
RMSNormEPS float32 `json:"rms_norm_eps"`
RopeTheta float32 `json:"rope_theta"`
ClampKQV float32 `json:"f_clamp_kqv"`
SlidingWindow uint32 `json:"sliding_window"`
LayerTypes []string `json:"layer_types"`
HiddenSize uint32 `json:"hidden_size"`
NumHiddenLayers uint32 `json:"num_hidden_layers"`
IntermediateSize uint32 `json:"intermediate_size"`
NumAttentionHeads uint32 `json:"num_attention_heads"`
NumKeyValueHeads uint32 `json:"num_key_value_heads"`
MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
RMSNormEPS float32 `json:"rms_norm_eps"`
RopeTheta float32 `json:"rope_theta"`
RopeScaling *ropeScaling `json:"rope_scaling"`
ClampKQV float32 `json:"f_clamp_kqv"`
SlidingWindow uint32 `json:"sliding_window"`
LayerTypes []string `json:"layer_types"`
}
var _ ModelConverter = (*olmoModel)(nil)
func (p *olmoModel) KV(t *Tokenizer) ggml.KV {
kv := p.ModelParameters.KV(t)
kv["general.architecture"] = "olmo"
kv["olmo.block_count"] = p.NumHiddenLayers
kv["olmo.context_length"] = p.MaxPositionEmbeddings
kv["olmo.embedding_length"] = p.HiddenSize
kv["olmo.feed_forward_length"] = p.IntermediateSize
kv["olmo.attention.head_count"] = p.NumAttentionHeads
kv["olmo.attention.head_count_kv"] = cmp.Or(p.NumKeyValueHeads, p.NumAttentionHeads)
kv["general.architecture"] = "olmo2"
kv["olmo2.block_count"] = p.NumHiddenLayers
kv["olmo2.context_length"] = p.MaxPositionEmbeddings
kv["olmo2.embedding_length"] = p.HiddenSize
kv["olmo2.feed_forward_length"] = p.IntermediateSize
kv["olmo2.attention.head_count"] = p.NumAttentionHeads
kv["olmo2.attention.head_count_kv"] = cmp.Or(p.NumKeyValueHeads, p.NumAttentionHeads)
if p.RopeTheta > 0 {
kv["olmo.rope.freq_base"] = p.RopeTheta
kv["olmo2.rope.freq_base"] = p.RopeTheta
} else {
kv["olmo.rope.freq_base"] = float32(10000.0)
kv["olmo2.rope.freq_base"] = float32(10000.0)
}
if p.RopeScaling != nil {
if p.RopeScaling.Factor > 0 {
kv["olmo2.rope.scaling.factor"] = p.RopeScaling.Factor
}
if p.RopeScaling.OriginalMaxPositionEmbeds > 0 {
kv["olmo2.rope.scaling.original_context_length"] = p.RopeScaling.OriginalMaxPositionEmbeds
}
if p.RopeScaling.AttentionFactor > 0 {
kv["olmo2.rope.scaling.attn_factor"] = p.RopeScaling.AttentionFactor
}
if p.RopeScaling.RopeType != "" {
kv["olmo2.rope.scaling.type"] = p.RopeScaling.RopeType
}
}
if p.RMSNormEPS > 0 {
kv["olmo.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
kv["olmo2.attention.layer_norm_rms_epsilon"] = p.RMSNormEPS
}
if p.ClampKQV > 0 {
kv["olmo.attention.clamp_kqv"] = p.ClampKQV
kv["olmo2.attention.clamp_kqv"] = p.ClampKQV
}
if p.SlidingWindow > 0 {
kv["olmo.attention.sliding_window"] = p.SlidingWindow
kv["olmo2.attention.sliding_window"] = p.SlidingWindow
}
if len(p.LayerTypes) > 0 {
kv["olmo.attention.layer_types"] = p.LayerTypes
slidingPattern := make([]bool, len(p.LayerTypes))
for i, layerType := range p.LayerTypes {
slidingPattern[i] = (layerType == "sliding_attention")
}
kv["olmo2.attention.sliding_window_pattern"] = slidingPattern
}
return kv