ollama/convert/convert_commandr.go
Josh 93a8daf285
convert: import support for command-r models from safetensors (#6063)
---------

Co-authored-by: Patrick Devine <patrick@infrahq.com>
2025-01-15 16:31:22 -08:00

77 lines
2.3 KiB
Go

package convert
import (
"cmp"
"github.com/ollama/ollama/llm"
)
type commandrModel 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"`
LayerNormEPS float32 `json:"layer_norm_eps"`
RopeTheta float32 `json:"rope_theta"`
UseQKNorm bool `json:"use_qk_norm"`
MaxLength uint32 `json:"model_max_length"`
LogitScale float32 `json:"logit_scale"`
NCtx uint32 `json:"n_ctx"`
}
var _ ModelConverter = (*commandrModel)(nil)
func (p *commandrModel) KV(t *Tokenizer) llm.KV {
kv := p.ModelParameters.KV(t)
kv["general.architecture"] = "command-r"
kv["general.name"] = "command-r"
kv["command-r.context_length"] = cmp.Or(p.MaxLength, p.MaxPositionEmbeddings, p.NCtx)
kv["command-r.embedding_length"] = p.HiddenSize
kv["command-r.block_count"] = p.HiddenLayers
kv["command-r.feed_forward_length"] = p.IntermediateSize
kv["command-r.attention.head_count"] = p.NumAttentionHeads
kv["command-r.attention.head_count_kv"] = p.NumKeyValueHeads
kv["command-r.attention.layer_norm_epsilon"] = p.LayerNormEPS
kv["command-r.rope.freq_base"] = p.RopeTheta
kv["command-r.max_position_embeddings"] = cmp.Or(p.MaxLength, p.MaxPositionEmbeddings)
kv["command-r.logit_scale"] = p.LogitScale
kv["command-r.rope.scaling.type"] = "none"
return kv
}
func (p *commandrModel) 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 *commandrModel) Replacements() []string {
return []string{
"self_attn.q_norm", "attn_q_norm",
"self_attn.k_norm", "attn_k_norm",
"model.layers", "blk",
"input_layernorm", "attn_norm",
"mlp.down_proj", "ffn_down",
"mlp.gate_proj", "ffn_gate",
"mlp.up_proj", "ffn_up",
"self_attn.k_proj", "attn_k",
"self_attn.o_proj", "attn_output",
"self_attn.q_proj", "attn_q",
"self_attn.v_proj", "attn_v",
"model.norm", "output_norm",
"model.embed_tokens", "token_embd",
}
}