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: 878 Bytes
bd9d474 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | {
"architectures": [
"Qwen3Model"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2560,
"initializer_range": 0.02,
"intermediate_size": 9728,
"max_position_embeddings": 40960,
"max_window_layers": 36,
"model_type": "qwen3",
"num_attention_heads": 32,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
"quantization": {
"group_size": 64,
"bits": 6,
"mode": "affine"
},
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000,
"sliding_window": null,
"tie_word_embeddings": true,
"torch_dtype": "bfloat16",
"transformers_version": "4.51.0",
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 151936
} |