Instructions to use Giovanniwants/YanahCoder-Qwen3-Coder-Next with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Giovanniwants/YanahCoder-Qwen3-Coder-Next with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download Giovanniwants/YanahCoder-Qwen3-Coder-Next --local-dir YanahCoder-Qwen3-Coder-Next
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
YanahCoder model files (Qwen3-Coder-Next, streamed-experts layout)
Files for the YanahCoder Mac app, which streams the mixture-of-experts weights from SSD instead of holding them in RAM.
dense/: the non-expert weights of a 4-bit MLX conversion (attention, Gated DeltaNet, shared experts, embeddings, routers), the tokenizer and config.experts-4bit/: routed experts (4-bit), one record per expert, split into one file per layer.yanah_manifest.json: size and sha256 of every file.
Derived from Qwen/Qwen3-Coder-Next (Apache-2.0). Quantization reduces quality versus the original model.
Hardware compatibility
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Quantized
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Model tree for Giovanniwants/YanahCoder-Qwen3-Coder-Next
Base model
Qwen/Qwen3-Coder-Next