GGUF quantizations of empero-ai/Qwen3.8-2B — a full-parameter distillation of Qwen3.8 2.4T A95B into the Qwen3.5-2B architecture, the smallest member of the family — 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	Qwen3.5-2B (base)	Qwen3.8-2B	Δ
mmlu (CoT, 57 subjects)	0.283	0.548	+0.265
gsm8k_cot	0.330	0.640	+0.310

[!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
`Qwen3.8-2B-Q4_K_M.gguf`	Q4_K_M	1.312 GB	Recommended. Best quality/size balance; runs on phones and SBCs.
`Qwen3.8-2B-Q5_K_M.gguf`	Q5_K_M	1.455 GB	Higher quality at a modest size increase.
`Qwen3.8-2B-Q6_K.gguf`	Q6_K	1.606 GB	Near-lossless.
`Qwen3.8-2B-Q8_0.gguf`	Q8_0	2.077 GB	Highest-quality quantization.
`Qwen3.8-2B-BF16.gguf`	BF16	3.897 GB	Full precision reference.

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

Where it runs

Practical weight-size-based guidance at modest context — the KV cache is the dominant cost at long context:

Quant	Guidance
Q4_K_M / Q5_K_M	Phones, single-board computers, any modern laptop — CPU-only is entirely usable at this scale.
Q6_K / Q8_0	Any 4 GB+ GPU, or CPU with 8 GB RAM.
BF16	6 GB+ GPU.

Usage

llama.cpp

bash

llama-cli -m Qwen3.8-2B-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/Qwen3.8-2B, a distillation of Qwen3.8 2.4T A95B into Qwen/Qwen3.5-2B trained on ~30,000 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

Support / Donate

If this model helped you, consider supporting the project:

BTC: bc1qx6zepu6sfkvshgdmc4ewu6pk6rpadvpgffpp7v
LTC: ltc1qv2mefzps2vtjcpwfx8xxdrpplrcvltswm68r7x

Acknowledgements

Developed and released by Empero
Base model: Qwen3.5-2B (Alibaba Qwen team)
GGUF quantization: llama.cpp (ggml-org)

JeXTCy1/Qwen3.8-2B-Distill-GGUF

Qwen3.8-2B — GGUF