Feature Extraction
sentence-transformers
Safetensors
Transformers
English
qwen3
text-embeddings-inference
Instructions to use codefuse-ai/F2LLM-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use codefuse-ai/F2LLM-0.6B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codefuse-ai/F2LLM-0.6B") 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] - Transformers
How to use codefuse-ai/F2LLM-0.6B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="codefuse-ai/F2LLM-0.6B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/F2LLM-0.6B") model = AutoModel.from_pretrained("codefuse-ai/F2LLM-0.6B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- 1_Pooling/config.json +10 -0
- config_sentence_transformers.json +8 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 1024,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": false,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": true,
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"include_prompt": true
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}
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config_sentence_transformers.json
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{
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"prompts": {
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"query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:",
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"document": ""
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},
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"default_prompt_name": null,
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"similarity_fn_name": "cosine"
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}
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