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empero-ai/Qwen3.8-9B-Distill-GGUF

sourceHugging Faceapache-2.0updated 1mo agoView on Hugging Face
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Model Card

Qwen3.8-9B — GGUF

Developed by [Empero](https://empero.org)

GGUF quantizations of [empero-ai/Qwen3.8-9B](https://huggingface.co/empero-ai/Qwen3.8-9B) — a full-parameter distillation of Qwen3.8 2.4T A95B into the Qwen3.5-9B 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](https://huggingface.co/empero-ai/Qwen3.8-9B).

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

TaskQwen3.5-9B (base)**Qwen3.8-9B**Δ
mmlu (CoT, 57 subjects)0.5460.751+0.205
gsm8k_cot0.8850.870−0.015
[!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

FileQuantSizeNotes
Qwen3.8-9B-Q4_K_M.ggufQ4KM5.780 GBRecommended. Best quality/size balance for most users.
Qwen3.8-9B-Q5_K_M.ggufQ5KM6.643 GBHigher quality, still fits an 8 GB card at short context.
Qwen3.8-9B-Q6_K.ggufQ6_K7.559 GBNear-lossless.
Qwen3.8-9B-Q8_0.ggufQ8_09.786 GBHighest-quality quantization.
Qwen3.8-9B-BF16.ggufBF1618.407 GBFull 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:

QuantGuidance
Q4KM / Q5KMComfortable on 8–12 GB cards for everyday use.
Q6K / Q8012–16 GB recommended.
BF1624 GB+.

Usage

llama.cpp

bash
llama-cli -m Qwen3.8-9B-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 <think> block, so allow a generous -n and strip the <think>...</think> 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/Qwen3.8-9B](https://huggingface.co/empero-ai/Qwen3.8-9B), a distillation of Qwen3.8 2.4T A95B into Qwen/Qwen3.5-9B trained on ~70,000 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](https://empero.org) for releases, evals, and research notes.

Support / Donate

If this model helped you, consider supporting the project:

  • —BTC: bc1qx6zepu6sfkvshgdmc4ewu6pk6rpadvpgffpp7v
  • —LTC: ltc1qv2mefzps2vtjcpwfx8xxdrpplrcvltswm68r7x

Acknowledgements