Instructions to use stanrom/ShareCaptioner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stanrom/ShareCaptioner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="stanrom/ShareCaptioner", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("stanrom/ShareCaptioner", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update modeling_InternLM_XComposer.py
Browse files
modeling_InternLM_XComposer.py
CHANGED
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@@ -66,9 +66,9 @@ class InternLMXComposerForCausalLM(PreTrainedModel):
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assert int(torch.__version__[0]) == 2
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# speed up init llm
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with torch.device('meta'):
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self.internlm_model = InternLMForCausalLM._from_config(config)
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# self.internlm_model.to_empty(device=config.device).to(torch.float16)
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self.internlm_model.tie_weights()
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# self.internlm_model.to(config.device)
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self.internlm_proj = nn.Linear(4096,
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assert int(torch.__version__[0]) == 2
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# speed up init llm
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with torch.device('meta'):
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+
self.internlm_model = InternLMForCausalLM._from_config(config)
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# self.internlm_model.to_empty(device=config.device).to(torch.float16)
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# self.internlm_model.tie_weights()
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# self.internlm_model.to(config.device)
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self.internlm_proj = nn.Linear(4096,
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