Feature Extraction
Transformers
Safetensors
sentence-transformers
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c2llm
code
custom_code
Instructions to use codefuse-ai/C2LLM-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codefuse-ai/C2LLM-0.5B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="codefuse-ai/C2LLM-0.5B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("codefuse-ai/C2LLM-0.5B", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use codefuse-ai/C2LLM-0.5B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codefuse-ai/C2LLM-0.5B", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Update configuration_c2llm.py
Browse files- configuration_c2llm.py +1 -1
configuration_c2llm.py
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@@ -24,7 +24,7 @@ class C2LLMConfig(PretrainedConfig):
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self,
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attention_dropout=0.0,
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bos_token_id=151643,
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eos_token_id=
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hidden_act="silu",
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hidden_size=896,
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initializer_range=0.02,
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self,
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attention_dropout=0.0,
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bos_token_id=151643,
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+
eos_token_id=151645,
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hidden_act="silu",
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hidden_size=896,
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initializer_range=0.02,
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