dynamically determine num_layers in _qwen3_hf_to_vllm #199
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| hf_state_dict = self.engine.checkpointer.sd_adapter.to_hf(flattened_state_dict) | ||
| # TODO: Figure out how to gracefully handle which model to-vLLM conversion is needed | ||
| vllm_ready_hf_sd = _qwen3_hf_to_vllm(sd=hf_state_dict, num_layers=28) | ||
| vllm_ready_hf_sd = _qwen3_hf_to_vllm(sd=hf_state_dict) |
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Could this just be simplified by using num_layers=self.model.config.num_hidden_layers? see this
Ideally you should not be needing this method in the trainer at all. The trainer should be agnostic to the type/arch of generator.
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this should work with torchtitan:
num_layers=self.engine.model_args.n_layers)
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I guess simply reading the max num layers from regex has the advantage that it's agnostic of the trainer implementation as long as the state dict is in huggingface format.
Let me know what you think. @Ritesh1905 @allenwang28
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closed since the change is already in #215 |
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This is fixing #190