Text Generation
MLX
Safetensors
mistral3
rotorquant
kv-cache-quantization
2-bit
weight-quantization
leanstral
lean4
formal-proofs
theorem-proving
quantized
apple-silicon
mistral
Mixture of Experts
Instructions to use majentik/Leanstral-RotorQuant-MLX-2bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use majentik/Leanstral-RotorQuant-MLX-2bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("majentik/Leanstral-RotorQuant-MLX-2bit") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use majentik/Leanstral-RotorQuant-MLX-2bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "majentik/Leanstral-RotorQuant-MLX-2bit" --prompt "Once upon a time"
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| "image_token_index": 10, | |
| "model_type": "mistral3", | |
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| "projector_hidden_act": "gelu", | |
| "quantization": { | |
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| "initializer_range": 0.02, | |
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| "kv_lora_rank": 256, | |
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