Text Classification
Transformers
Safetensors
English
Chinese
internlm2
feature-extraction
reward model
custom_code
Instructions to use internlm/internlm2-20b-reward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use internlm/internlm2-20b-reward with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="internlm/internlm2-20b-reward", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("internlm/internlm2-20b-reward", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
x54-729 commited on
Commit ·
e04e906
1
Parent(s): 5a485b0
replace get_max_length
Browse files- modeling_internlm2.py +3 -3
modeling_internlm2.py
CHANGED
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@@ -1077,7 +1077,7 @@ class InternLM2Model(InternLM2PreTrainedModel):
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min_dtype = torch.finfo(dtype).min
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sequence_length = input_tensor.shape[1]
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if using_static_cache:
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-
target_length = past_key_values.
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else:
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target_length = (
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attention_mask.shape[-1]
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@@ -1266,8 +1266,8 @@ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
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if isinstance(past_key_values, Cache):
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past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
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max_cache_length = (
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-
torch.tensor(past_key_values.
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-
if past_key_values.
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else None
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)
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cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length)
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min_dtype = torch.finfo(dtype).min
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sequence_length = input_tensor.shape[1]
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if using_static_cache:
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+
target_length = past_key_values.get_max_cache_shape()
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else:
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target_length = (
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attention_mask.shape[-1]
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if isinstance(past_key_values, Cache):
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past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
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max_cache_length = (
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+
torch.tensor(past_key_values.get_max_cache_shape(), device=input_ids.device)
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+
if past_key_values.get_max_cache_shape() is not None
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else None
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)
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cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length)
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