add Yi-VL-34B model
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@ -166,7 +166,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
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| [StarCoder2](https://huggingface.co/bigcode) | 3B/7B/15B | q_proj,v_proj | - |
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| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | q_proj,v_proj | xverse |
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| [Yi (1/1.5)](https://huggingface.co/01-ai) | 6B/9B/34B | q_proj,v_proj | yi |
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| [Yi-VL](https://huggingface.co/01-ai) | 6B | q_proj,v_proj | yi_vl |
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| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | q_proj,v_proj | yi_vl |
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| [Yuan](https://huggingface.co/IEITYuan) | 2B/51B/102B | q_proj,v_proj | yuan |
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> [!NOTE]
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@ -166,7 +166,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
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| [StarCoder2](https://huggingface.co/bigcode) | 3B/7B/15B | q_proj,v_proj | - |
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| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | q_proj,v_proj | xverse |
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| [Yi (1/1.5)](https://huggingface.co/01-ai) | 6B/9B/34B | q_proj,v_proj | yi |
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| [Yi-VL](https://huggingface.co/01-ai) | 6B | q_proj,v_proj | yi_vl |
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| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | q_proj,v_proj | yi_vl |
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| [Yuan](https://huggingface.co/IEITYuan) | 2B/51B/102B | q_proj,v_proj | yuan |
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> [!NOTE]
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@ -1215,6 +1215,9 @@ register_model_group(
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"YiVL-6B-Chat": {
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DownloadSource.DEFAULT: "BUAADreamer/Yi-VL-6B-hf",
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},
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"YiVL-34B-Chat": {
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DownloadSource.DEFAULT: "BUAADreamer/Yi-VL-34B-hf",
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},
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},
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template="yi_vl",
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vision=True,
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@ -78,8 +78,15 @@ def load_tokenizer(model_args: "ModelArguments") -> "TokenizerModule":
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patch_tokenizer(tokenizer)
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if model_args.visual_inputs:
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try:
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processor = AutoProcessor.from_pretrained(model_args.model_name_or_path, **init_kwargs)
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setattr(processor, "tokenizer", tokenizer)
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except Exception:
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raise ValueError(
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"This multimodal LLM is not supported.\n"
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"Download LLaVA-1.5 models from: https://huggingface.co/llava-hf\n"
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"Download Yi-VL models from: https://huggingface.co/BUAADreamer"
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)
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else:
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processor = None
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@ -58,7 +58,7 @@ class LlavaMultiModalProjectorForYiVLForVLLM(LlavaMultiModalProjectorForYiVL):
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self.linear_2 = torch.nn.LayerNorm(text_hidden_size, bias=True)
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self.linear_3 = torch.nn.Linear(text_hidden_size, text_hidden_size, bias=True)
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self.linear_4 = torch.nn.LayerNorm(text_hidden_size, bias=True)
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self.act = torch.nn.GELU()
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self.act = ACT2FN[projector_hidden_act]
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def autocast_projector_dtype(
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