Instructions to use TiGa-RCE/LFM2-1.2B-MLX-oQ8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TiGa-RCE/LFM2-1.2B-MLX-oQ8 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir LFM2-1.2B-MLX-oQ8 TiGa-RCE/LFM2-1.2B-MLX-oQ8
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 1,172 Bytes
c0b11ae | 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 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | {
"architectures": [
"Lfm2ForCausalLM"
],
"block_auto_adjust_ff_dim": true,
"block_dim": 2048,
"block_ff_dim": 12288,
"block_ffn_dim_multiplier": 1.0,
"block_mlp_init_scale": 1.0,
"block_multiple_of": 256,
"block_norm_eps": 1e-05,
"block_out_init_scale": 1.0,
"block_use_swiglu": true,
"block_use_xavier_init": true,
"bos_token_id": 1,
"conv_L_cache": 3,
"conv_bias": false,
"conv_dim": 2048,
"conv_dim_out": 2048,
"conv_use_xavier_init": true,
"eos_token_id": 7,
"full_attn_idxs": [
2,
5,
8,
10,
12,
14
],
"hidden_size": 2048,
"initializer_range": 0.02,
"max_position_embeddings": 128000,
"model_type": "lfm2",
"norm_eps": 1e-05,
"num_attention_heads": 32,
"num_heads": 32,
"num_hidden_layers": 16,
"num_key_value_heads": 8,
"pad_token_id": 0,
"rope_theta": 1000000.0,
"torch_dtype": "bfloat16",
"transformers_version": "4.54.0.dev0",
"use_cache": true,
"use_pos_enc": true,
"vocab_size": 65536,
"quantization": {
"group_size": 64,
"bits": 8,
"mode": "affine"
},
"quantization_config": {
"group_size": 64,
"bits": 8,
"mode": "affine"
}
} |