Added support for DeepseekV2 model #382
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Apologies for the wait
I think this is a good first stab but will need a bit more work to push it over the finish line.
Please see the comments and once you test it on your end it would be beneficial for both parties if you could mention how you tested it. It could be as simple as a yaml file for linear merging where you use the DeepseekV2
You've probably noticed the failing formatting check, so just be explicit please do address those
| "layer_templates": { | ||
| "weights": [ | ||
| { | ||
| "name" : "model.layers.${layer_index}.self_attn.q_proj.weight" |
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Unless I'm mistaken I don't think this weight exists in the model. Please see here: https://huggingface.co/deepseek-ai/DeepSeek-V2/raw/main/model.safetensors.index.json
"model.layers.0.self_attn.q_a_proj.weight": "model-00001-of-000055.safetensors",
"model.layers.0.self_attn.q_a_layernorm.weight": "model-00001-of-000055.safetensors",
"model.layers.0.self_attn.q_b_proj.weight": "model-00001-of-000055.safetensors",
| def layer_weights( | ||
| self, index: int, config: PretrainedConfig | ||
| ) -> Optional[List[WeightInfo]]: | ||
| num_experts = self.num_local_experts |
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Nit: Any reason for local aliasing? I see it is also present in the Mixtral code, but any reason you left it in?
| DEEPSEEKV2_INFO = _load_json_arch("deepseekv2.json") | ||
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| def get_architecture_info(config: PretrainedConfig) -> ArchitectureInfo: |
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Unless I am mistaken which I could be, there needs to be a clause within the get_architecture_info function similar to Mixtral's otherwise the code https://github.com/arcee-ai/mergekit/blob/main/mergekit/merge.py#L51 will just pull in just the info associated with the template
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@aditya-29 can you please respond :) sorry for the late reply. |
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Thanks @metric-space and @shamanez. I didn't get a chance to go over the comments earlier. I will work on the suggested changes and reach out to you for any clarification |
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Thanks a lot mate.
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@cg123 is there plan to support DeepseekV2 and DeepseekV3? |
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This pull request introduces the capability to merge DeepSeekV2 Mixture-of-Experts (MoE) models using MergeKit. To facilitate this, a
deepseekv2.jsonconfiguration file has been added to the architecture directory. Additionally, a custom class analogous to Mixtral has been implemented to enable model merging based on the JSON configuration.