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Unverified Commit f6f37130 authored by Bruce MacDonald's avatar Bruce MacDonald Committed by GitHub
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convert: qwen2 from safetensors (#8408) 

Add native support for converting Qwen2 family models (including Qwen2.5)
from safetensors to gguf format so we can run it.
parent a30f3472
Show whitespace changes
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Showing with 397 additions and 0 deletions
convert/convert.go
View file @ f6f37130
...@@ -187,6 +187,8 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error { ...@@ -187,6 +187,8 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
conv = &gemma2Model{} conv = &gemma2Model{}
case "Phi3ForCausalLM": case "Phi3ForCausalLM":
conv = &phi3Model{} conv = &phi3Model{}
case "Qwen2ForCausalLM":
conv = &qwen2Model{}
case "BertModel": case "BertModel":
conv = &bertModel{} conv = &bertModel{}
default: default:
......
convert/convert_qwen2.go 0 → 100644
View file @ f6f37130
package convert
import "github.com/ollama/ollama/llm"
type qwen2Model struct {
ModelParameters
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"`
RopeTheta float32 `json:"rope_theta"`
RopeScaling struct {
Type string `json:"type"`
Factor ropeFactor `json:"factor"`
OriginalMaxPositionEmbeddings uint32 `json:"original_max_position_embeddings"`
} `json:"rope_scaling"`
RMSNormEPS float32 `json:"rms_norm_eps"`
}
var _ ModelConverter = (*qwen2Model)(nil)
func (q *qwen2Model) KV(t *Tokenizer) llm.KV {
kv := q.ModelParameters.KV(t)
kv["general.architecture"] = "qwen2"
kv["qwen2.block_count"] = q.HiddenLayers
kv["qwen2.context_length"] = q.MaxPositionEmbeddings
kv["qwen2.embedding_length"] = q.HiddenSize
kv["qwen2.feed_forward_length"] = q.IntermediateSize
kv["qwen2.attention.head_count"] = q.NumAttentionHeads
kv["qwen2.attention.head_count_kv"] = q.NumKeyValueHeads
kv["qwen2.rope.freq_base"] = q.RopeTheta
kv["qwen2.attention.layer_norm_rms_epsilon"] = q.RMSNormEPS
switch q.RopeScaling.Type {
case "":
// no scaling
case "yarn":
kv["qwen2.rope.scaling.type"] = q.RopeScaling.Type
kv["qwen2.rope.scaling.factor"] = q.RopeScaling.Factor
default:
panic("unknown rope scaling type")
}
return kv
}
func (q *qwen2Model) Tensors(ts []Tensor) []llm.Tensor {
var out []llm.Tensor
for _, t := range ts {
out = append(out, llm.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),
WriterTo: t,
})
}
return out
}
func (p *qwen2Model) Replacements() []string {
return []string{
"lm_head", "output",
"model.embed_tokens", "token_embd",
"model.layers", "blk",
"input_layernorm", "attn_norm",
"self_attn.k_proj", "attn_k",
"self_attn.v_proj", "attn_v",
"self_attn.q_proj", "attn_q",
"self_attn.o_proj", "attn_output",
"mlp.down_proj", "ffn_down",
"mlp.gate_proj", "ffn_gate",
"mlp.up_proj", "ffn_up",
"post_attention_layernorm", "ffn_norm",
"model.norm", "output_norm",
}
}
convert/convert_test.go
View file @ f6f37130
...@@ -108,6 +108,7 @@ func TestConvertModel(t *testing.T) { ...@@ -108,6 +108,7 @@ func TestConvertModel(t *testing.T) {
"Phi-3-mini-128k-instruct", "Phi-3-mini-128k-instruct",
"all-MiniLM-L6-v2", "all-MiniLM-L6-v2",
"gemma-2-9b-it", "gemma-2-9b-it",
"Qwen2.5-0.5B-Instruct",
} }
for i := range cases { for i := range cases {
......
convert/testdata/Qwen2.5-0.5B-Instruct.json 0 → 100644
View file @ f6f37130
{
"general.architecture": "qwen2",
"general.file_type": "1",
"general.parameter_count": "494032768",
"general.quantization_version": "2",
"output_norm.weight": "93a01a6db3419e85320a244bbf8ae81c43033b1d10c342bea3797ff2ce348390",
"qwen2.attention.head_count": "14",
"qwen2.attention.head_count_kv": "2",
"qwen2.attention.layer_norm_rms_epsilon": "1e-06",
"qwen2.block_count": "24",
"qwen2.context_length": "32768",
"qwen2.embedding_length": "896",
"qwen2.feed_forward_length": "4864",
"qwen2.rope.freq_base": "1e+06",
"token_embd.weight": "d74257dc547b48be5ae7b93f1c9af072c0c42dbbb85503078e25c59cd09e68d0",
"tokenizer.ggml.add_eos_token": "false",
"tokenizer.ggml.add_padding_token": "false",
"tokenizer.ggml.eos_token_id": "151645",
"tokenizer.ggml.merges": "6b1b1c58f1223d74f9095929d3e6416cdd74784440221a5507b87b8197f2bfd2",
"tokenizer.ggml.model": "gpt2",
"tokenizer.ggml.padding_token_id": "151643",
"tokenizer.ggml.pre": "qwen2",
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"blk.7.attn_v.weight": "a142fe6e106d3ab484e2dc6f9c72b8fc0a385279dde08deb1ad1fd05ac25deb1",
"blk.7.ffn_down.weight": "8daf7e8c430d183a4d6ab3eb575fafa4b5e31689f68b290c8b370411ad9d0f12",
"blk.7.ffn_gate.weight": "a2a786b45eb660994254b48e2aaf22f3e9821cfb383dee0ba04cc4350a2f8e72",
"blk.7.ffn_norm.weight": "73828bbc8c9610cc139fcf03e96272648cdc291263251fe3a67367408deb69e1",
"blk.7.ffn_up.weight": "e85dd0f63fed449ce16893c5795ea6a050a2d7a66d9534410a227e22c905dafa",
"blk.8.attn_k.bias": "91a752a6e2c364e5ee6a015770fe289aece4911ae6c6bbfe74ac52f465465f93",
"blk.8.attn_k.weight": "99c069e92c43a2efb74e23188256b3cabbbe06399878e681ce203a05d5da378a",
"blk.8.attn_norm.weight": "c76d36d3cc06aa2a9edb1abf9f602bb7ed61ac9d61f8ef7ed736a1e619abe717",
"blk.8.attn_output.weight": "ee5ff156a2625e1f203f65e69b514f9df04bd9a5e82b28e3876e16cf1c6f65c5",
"blk.8.attn_q.bias": "8fbd868a93b330c8b0418b488c5301f42a7eb0c58445a4e515d56777f1d96ed5",
"blk.8.attn_q.weight": "9f20ef86e80098ba52a3a31ebcc315bea3a614dac9cba7ac1db02f156db9b577",
"blk.8.attn_v.bias": "c4813571d5d618742183a7890c0b89cd7f18e210c758f63aad564659bc38a26d",
"blk.8.attn_v.weight": "ea88e1a4cf8bd56e9a88ada427d2b0cd352234827640757ee2a9ed594fb67a53",
"blk.8.ffn_down.weight": "b0d1a7495811580b189aaa3e20ea871d6d01ed7b6c23e59825078ef786944ff2",
"blk.8.ffn_gate.weight": "0a17c0caa0b06721c49b59b2a63a5dcbf744dd1cffa55962b404ba910c658a62",
"blk.8.ffn_norm.weight": "f15f109d4a8e9d1ff7c71fa5bc6373df7ee80c5f7d1de3fa0d4849d747e36bcb",
"blk.8.ffn_up.weight": "bbf4c5c4c5c8a0f9ae8b88e3cc8b86f81b98148722d5a350995af176c0b774f2",
"blk.9.attn_k.bias": "a7f60d962686b8ca60f69643e0e0fa8614688be738fb0b1c6bd54de35c2beb5e",
"blk.9.attn_k.weight": "dd80ce4adb00e338fc04b307e4c18a27071f4ba4397184a24d765e6e4a268ef4",
"blk.9.attn_norm.weight": "721e6487547e2b3986ab4b4e2500ceade59d908bccf4436e1e8031f246deb2bd",
"blk.9.attn_output.weight": "5a800af39107b363861e5f5173483cdcd644d8ac3b0c8a443b9c759d71285db8",
"blk.9.attn_q.bias": "0a19b4925ea8ca8067acc909b058adc327de3874cfc94cc9eb4a106d3f370123",
"blk.9.attn_q.weight": "93e84906684c0c7ede79967236d9fc8344da84a9f1daa04e8295c2c9b6b26a24",
"blk.9.attn_v.bias": "615421f812f821e230ecde4e6da35d868823248355ce7e4e51e2d650ead565f9",
"blk.9.attn_v.weight": "7f4913e289aefd9ceecbdaf9767b1e95303f5d59dd67ecb2cc15768477f4d08e",
"blk.9.ffn_down.weight": "95d1b3933221e87dc4af70dd566daec9498bf358070b8d26f1fc70766a84a152",
"blk.9.ffn_gate.weight": "530f2d04f6a1fbffaaa5f2fbc3a328ebed7b330e3af14b4fc7d8a51b13ad8d42",
"blk.9.ffn_norm.weight": "28077de416217ea1df94b96017bef4cc562ab62e51b1a03a671c70abc29ce52a",
"blk.9.ffn_up.weight": "b87b6190778aaee4695938e24ac6c90dbbee6dce7c5c2ab5bc26ba4564581822"
}
convert/tokenizer.go
View file @ f6f37130
...@@ -100,6 +100,8 @@ func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error) ...@@ -100,6 +100,8 @@ func parseTokenizer(fsys fs.FS, specialTokenTypes []string) (*Tokenizer, error)
t.Pre = "deepseek-llm" t.Pre = "deepseek-llm"
case "21cde974d587f0d54dc8d56b183cc1e6239600172035c68fbd6d4b9f8da0576e": case "21cde974d587f0d54dc8d56b183cc1e6239600172035c68fbd6d4b9f8da0576e":
t.Pre = "deepseek-coder" t.Pre = "deepseek-coder"
case "1ff7f41064896984db5d1bb6ff64fa4bc29007d08c1b439e505b7392777a319e":
t.Pre = "qwen2"
case "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855": case "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855":
// noop, empty pretokenizer // noop, empty pretokenizer
default: default:
......
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