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33ee7168ba
* WIP - MLX backend with gemma3 * MLX: add cmake and go tag build toggles To build the new MLX backend code: cmake --preset MLX cmake --build --preset MLX --parallel cmake --install build --component MLX go build -tags mlx . Note: the main.go entrypoint for the MLX engine will change in a follow up commit. * add experimental image generation runtime * add experimental image generation runtime * MLX: wire up cuda build for linux * MLX: get dependencies correct and dedup This is still too large for a unified github artifact, but is now "correct" for the mlx_cuda_v13 directory. * fix relative link bug in dedup * Add darwin build and readme * add go build tag for mlx dependent code and wire up build_darwin.sh * lint cleanup * macos: build mlx for x86 This will be CPU only. * cuda build instructions and fix drift from mlx bump * stale comment * Delete agent helper doc * Clean up readme.md * Revise README for tokenizer clarity and details Updated README to clarify tokenizer functionality and removed correctness section. --------- Co-authored-by: jmorganca <jmorganca@gmail.com>
77 lines
2.3 KiB
Go
77 lines
2.3 KiB
Go
package convert
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import (
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"cmp"
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"github.com/ollama/ollama/fs/ggml"
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)
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type commandrModel struct {
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ModelParameters
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MaxPositionEmbeddings uint32 `json:"max_position_embeddings"`
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HiddenSize uint32 `json:"hidden_size"`
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HiddenLayers uint32 `json:"num_hidden_layers"`
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IntermediateSize uint32 `json:"intermediate_size"`
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NumAttentionHeads uint32 `json:"num_attention_heads"`
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NumKeyValueHeads uint32 `json:"num_key_value_heads"`
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LayerNormEPS float32 `json:"layer_norm_eps"`
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RopeTheta float32 `json:"rope_theta"`
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UseQKNorm bool `json:"use_qk_norm"`
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MaxLength uint32 `json:"model_max_length"`
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LogitScale float32 `json:"logit_scale"`
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NCtx uint32 `json:"n_ctx"`
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}
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var _ ModelConverter = (*commandrModel)(nil)
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func (p *commandrModel) KV(t *Tokenizer) KV {
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kv := p.ModelParameters.KV(t)
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kv["general.architecture"] = "command-r"
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kv["general.name"] = "command-r"
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kv["command-r.context_length"] = cmp.Or(p.MaxLength, p.MaxPositionEmbeddings, p.NCtx)
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kv["command-r.embedding_length"] = p.HiddenSize
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kv["command-r.block_count"] = p.HiddenLayers
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kv["command-r.feed_forward_length"] = p.IntermediateSize
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kv["command-r.attention.head_count"] = p.NumAttentionHeads
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kv["command-r.attention.head_count_kv"] = p.NumKeyValueHeads
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kv["command-r.attention.layer_norm_epsilon"] = p.LayerNormEPS
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kv["command-r.rope.freq_base"] = p.RopeTheta
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kv["command-r.max_position_embeddings"] = cmp.Or(p.MaxLength, p.MaxPositionEmbeddings)
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kv["command-r.logit_scale"] = p.LogitScale
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kv["command-r.rope.scaling.type"] = "none"
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return kv
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}
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func (p *commandrModel) Tensors(ts []Tensor) []*ggml.Tensor {
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var out []*ggml.Tensor
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for _, t := range ts {
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out = append(out, &ggml.Tensor{
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Name: t.Name(),
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Kind: t.Kind(),
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Shape: t.Shape(),
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WriterTo: t,
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})
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}
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return out
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}
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func (p *commandrModel) Replacements() []string {
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return []string{
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"self_attn.q_norm", "attn_q_norm",
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"self_attn.k_norm", "attn_k_norm",
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"model.layers", "blk",
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"input_layernorm", "attn_norm",
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"mlp.down_proj", "ffn_down",
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"mlp.gate_proj", "ffn_gate",
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"mlp.up_proj", "ffn_up",
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"self_attn.k_proj", "attn_k",
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"self_attn.o_proj", "attn_output",
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"self_attn.q_proj", "attn_q",
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"self_attn.v_proj", "attn_v",
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"model.norm", "output_norm",
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"model.embed_tokens", "token_embd",
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}
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}
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