Instructions to use jdchang/test_rm_8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jdchang/test_rm_8b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jdchang/test_rm_8b", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jdchang/test_rm_8b", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update hf_utils.py
Browse files- hf_utils.py +1 -1
hf_utils.py
CHANGED
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@@ -268,7 +268,7 @@ class AutoModelForCausalLMWithRM(PreTrainedModel):
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return super().from_pretrained(
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pretrained_model_name_or_path,
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*model_args,
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-
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cache_dir,
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ignore_mismatched_sizes,
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force_download,
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return super().from_pretrained(
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pretrained_model_name_or_path,
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*model_args,
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+
pretrained_model_config,
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cache_dir,
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ignore_mismatched_sizes,
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force_download,
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