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
File size: 313 Bytes
bd9d474 | 1 2 3 4 5 6 7 8 9 10 | {
"word_embedding_dimension": 2560,
"pooling_mode_cls_token": false,
"pooling_mode_mean_tokens": false,
"pooling_mode_max_tokens": false,
"pooling_mode_mean_sqrt_len_tokens": false,
"pooling_mode_weightedmean_tokens": false,
"pooling_mode_lasttoken": true,
"include_prompt": true
} |