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Cataloged from empero-ai/Qwen3.8-9B-Distill-GGUF
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GGUF quantizations of 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.
Headline results for the source model (CoT protocols, lm-evaluation-harness, identical settings base vs. student):
[!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.
Sizes are exact decimal GB from the uploaded files (1 GB = 1,000,000,000 bytes).
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:
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.
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.
Quantizations of 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.
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If this model helped you, consider supporting the project:
bc1qx6zepu6sfkvshgdmc4ewu6pk6rpadvpgffpp7vltc1qv2mefzps2vtjcpwfx8xxdrpplrcvltswm68r7xllama-cli -m Qwen3.8-9B-Q4_K_M.gguf \ --temp 0.6 --top-p 0.95 --top-k 20 \ -n 16384 -cnv