Qwen3.8

About

What Qwen3.8 is

Qwen3.8-9B — GGUF Developed by Empero 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. Files 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: Usage llama.cpp 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, topp=0.95, topk=20. Provenance & licensing 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. 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 - Developed and released by Empero - Base model: Qwen3.5-9B (Alibaba Qwen team) - GGUF quantization: llama.cpp (ggml-org)

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys empero-ai-qwen3-8 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (empero-ai-qwen3-8 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"empero-ai-qwen3-8","messages":[{"role":"user","content":"Hello"}]}'

Details

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Licence

Open weights

Sources

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