Text Generation
MLX
Safetensors
English
Chinese
Mixture of Experts
mixture-of-experts
minimax_m2
quantized
apple-silicon
turboquant
jangtq
jangtq2
reap
Instructions to use OsaurusAI/MiniMax-M2.7-Small-JANGTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OsaurusAI/MiniMax-M2.7-Small-JANGTQ 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("OsaurusAI/MiniMax-M2.7-Small-JANGTQ") 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 OsaurusAI/MiniMax-M2.7-Small-JANGTQ with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "OsaurusAI/MiniMax-M2.7-Small-JANGTQ" --prompt "Once upon a time"
docs: add re-download warning for late-added jangtq_runtime.safetensors codebook sidecar
Browse files
README.md
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<p align="center">
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<a href="https://osaurus.ai"><img src="./osaurus-x-banner.png" alt="Osaurus AI"></a>
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- jangtq2
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---
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> ## ⚠️ IMPORTANT — Re-download required if you pulled before 2026-04-25
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>
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> This bundle was published in two commits: the initial commit had only the
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> quantized weight shards, and the codebook + Hadamard-rotation sidecar
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> (`jangtq_runtime.safetensors`) was uploaded ~2.1h later.
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>
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> **If your local copy is missing `jangtq_runtime.safetensors`,** the Swift
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> JANGTQ loader (and any decoder that reads codebooks from a sidecar
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> rather than re-deriving them at load time) will produce incoherent output.
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>
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> **Action:** re-pull the bundle (or just download the single file):
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> ```bash
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> hf download OsaurusAI/MiniMax-M2.7-Small-JANGTQ jangtq_runtime.safetensors --local-dir <your-dir>
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> ```
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> Bundles without this file but with `model.safetensors.index.json` listing
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> only `*.tq_packed`/`.tq_norms`/`.tq_bits` triplets are also fine for the
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> **Python** runtime — codebooks are recomputed from `(in_features, bits)`
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> via `jang_tools.turboquant.codebook.compute_codebook` at load time. The
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> sidecar exists for the Swift native loader.
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<p align="center">
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<a href="https://osaurus.ai"><img src="./osaurus-x-banner.png" alt="Osaurus AI"></a>
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