Instructions to use twainsk/qev-230m-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use twainsk/qev-230m-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir qev-230m-mlx twainsk/qev-230m-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 815 Bytes
b129ebd | 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 | {
"format": "qev",
"format_version": 3,
"family": "lfm2",
"runtime": "mlx",
"backend": "mlx_vlm",
"model_name": "qev-230m",
"foundation_architecture": "Lfm2ForCausalLM",
"base_model": "LiquidAI/LFM2.5-230M",
"base_revision": "40cb2ad3b3044d5a41eee083a6103c8b523afa45",
"modalities": [
"text"
],
"native_generation": "adapter_disabled",
"question_isolation": "independent_rows",
"hidden_size": 1024,
"pointer_dim": 256,
"pointer_bias": false,
"lora_rank": 64,
"lora_alpha": 128,
"lora_targets": [
"q_proj",
"k_proj",
"v_proj",
"out_proj",
"in_proj",
"w1",
"w2",
"w3"
],
"max_length": 1024,
"max_state": 384,
"multimodal_max_length": 8192,
"temperature": 4.5947934199881395,
"training_label": "t230-a",
"mlx_dtype": "bfloat16"
}
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