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empero-ai/Qwen3.8-4B-Distill-GGUF

🤗 On Hugging Facetext-generationapache-2.023 GBGGUF✓ HF checksums availableupdated today
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Qwen3.8-4B — GGUF

Developed by Empero

GGUF quantizations of empero-ai/Qwen3.8-4B — a full-parameter distillation of Qwen3.8 2.4T A95B into the Qwen3.5-4B architecture — for llama.cpp, Ollama, LM Studio, Jan, KoboldCpp, and other stock GGUF runtimes.

This card is about choosing a file and running it. The capability writeup, full benchmark results, and best practices live on the main model card.

Headline results for the source model (CoT protocols, lm-evaluation-harness, identical settings base vs. student):

| Task | Qwen3.5-4B (base) | Qwen3.8-4B | Δ |

|---|---:|---:|---:|

| mmlu (CoT, 57 subjects) | 0.354 | 0.553 | +0.199 |

| gsm8k_cot | 0.850 | 0.785 | −0.065 |

[!Note]
Qwen3.5-class models are hybrids: three Gated DeltaNet layers for every full-attention layer. A recent llama.cpp build with Qwen3.5 / Gated DeltaNet support is required — older builds will fail to load the architecture.

Files

| File | Quant | Size | Notes |

|---|---|---:|---|

| Qwen3.8-4B-Q4_K_M.gguf | Q4_K_M | 2.783 GB | Recommended. Best quality/size balance for most users. |

| Qwen3.8-4B-Q5_K_M.gguf | Q5_K_M | 3.161 GB | Higher quality at a modest size increase. |

| Qwen3.8-4B-Q6_K.gguf | Q6_K | 3.563 GB | Near-lossless. |

| Qwen3.8-4B-Q8_0.gguf | Q8_0 | 4.611 GB | Highest-quality quantization. |

| Qwen3.8-4B-BF16.gguf | BF16 | 8.666 GB | Full precision reference. |

Sizes are exact decimal GB from the uploaded files (1 GB = 1,000,000,000 bytes).

What fits on a GPU?

Practical weight-size-based guidance at modest context — the KV cache is the dominant cost at long context and may require offload regardless of weight quant:

| Quant | Guidance |

|---|---|

| Q4_K_M / Q5_K_M | Comfortable on 4–6 GB cards; strong CPU-only option as well. |

| Q6_K / Q8_0 | 6–8 GB recommended. |

| BF16 | 12 GB+. |

Usage

llama.cpp

llama-cli -m Qwen3.8-4B-Q4_K_M.gguf \
  --temp 0.6 --top-p 0.95 --top-k 20 \
  -n 16384 -cnv

Use the built-in chat template (-cnv). The model is a reasoning model: every answer opens with a ` block, so allow a generous -n and strip the ...` span for end users.

Ollama / LM Studio / Jan / KoboldCpp

Download the GGUF of your choice and load it directly; the chat template is embedded in the file. Recommended sampling: temperature=0.6, top_p=0.95, top_k=20.

Provenance & licensing

Quantizations of empero-ai/Qwen3.8-4B, a distillation of Qwen3.8 2.4T A95B into Qwen/Qwen3.5-4B trained on ~45,000 curated teacher traces from our internal Qwen3.8 distillation datasets. Weights are Apache-2.0, inherited from the Qwen base, shared as-is.

Stay in the loop

Sign up for the Empero newsletter at empero.org for releases, evals, and research notes.

Support / Donate

If this model helped you, consider supporting the project:

  • BTC: bc1qx6zepu6sfkvshgdmc4ewu6pk6rpadvpgffpp7v
  • LTC: ltc1qv2mefzps2vtjcpwfx8xxdrpplrcvltswm68r7x

Acknowledgements