Feature Extraction
MLX
Safetensors
sentence-transformers
qwen3
mlx-embeddings
text-embeddings-inference
Instructions to use fcmeyer/F2LLM-v2-4B-mlx-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use fcmeyer/F2LLM-v2-4B-mlx-6bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir F2LLM-v2-4B-mlx-6bit fcmeyer/F2LLM-v2-4B-mlx-6bit
- sentence-transformers
How to use fcmeyer/F2LLM-v2-4B-mlx-6bit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("fcmeyer/F2LLM-v2-4B-mlx-6bit") 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
- Local Apps Settings
- LM Studio
- Atomic Chat
- Xet hash:
- e6e9eecede8f3127d716d0f7cf436334e11243c342b3a110ffb34b7e93c511a2
- Size of remote file:
- 11.4 MB
- SHA256:
- 24d4dfb58cd7498c4651780628269e16f3ecb6d995edf2ce952b455093983d04
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