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EXL3 · 3.0 bpw · 15.3 GB · Mixture‑of‑Experts · 48 layers × 256 experts[!NOTE] An ExLlamaV3 build oflordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilledat 3.0 bits per weight — the tightest fit — sized for 16 GB consumer cards while leaving usable context room. See Quants for sibling repos at other bit‑widths or browse the collection.
| Loader | Use it for |
|---|---|
| TabbyAPI | OpenAI‑compatible HTTP server. Drop‑in for OpenAI clients. |
| text‑generation‑webui | Local chat UI. Pick the ExLlamaV3 loader from the model dropdown. |
| ExLlamaV3 | Direct Python API for embedding the model in your own code or pipeline. |
[!TIP] VRAM at 3.0 bpw: weights on disk + ~2 GB context overhead. Fits a 16 GB card with workable context, comfortable on 24 GB with very long context.
1pip install -U huggingface_hub
2
3hf download \
4 blockblockblock/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-exl3-3.0bpw \
5 --local-dir ./Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-exl3-3.0bpwquantization_config.json)| Setting | Value |
|---|---|
| Format | EXL3 |
| Bits per weight | 3.0 |
| Head bits | 8 |
| Calibration rows | 128 |
| Codebook | MCG |
| Out‑scales | always |
| Parallel mode | enabled (MoE expert batching) |
[!IMPORTANT] Use and license follow the base model. Quantization adds no additional restrictions. Refer to the upstream repository for terms, citation, and safety documentation.
{org}/{model}-exl3-{bpw}bpw