Text Generation
MLX
Safetensors
mistral3
rotorquant
kv-cache-quantization
8bit
weight-quantization
leanstral
lean4
formal-proofs
theorem-proving
quantized
apple-silicon
mistral
Mixture of Experts
8-bit precision
Instructions to use majentik/Leanstral-RotorQuant-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use majentik/Leanstral-RotorQuant-MLX-8bit 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-8bit") 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-8bit 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-8bit" --prompt "Once upon a time"
- Atomic Chat
Commit History
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Remove links to retired duplicate brand variants
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Card accuracy pass 3: remove brand-comparison claim tables
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Card accuracy pass 2: remove unmeasured speed claims, honest brand labels
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Card accuracy sweep: honest brand labeling, remove dead links, upstream KV tip
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Remove links to retired card-only KV-docs repos
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docs: upstream-first KV-cache guidance (q8_0/q4_0, mainline Hadamard rotation); fork demoted to experimental
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docs: Tier 2 polish — variant matrix + quant trade-off
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chore(card): enrich YAML frontmatter (pipeline_tag, language, library_name, inference)
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Add MLX 8-bit quantized model with KV cache compression
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initial commit
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