Instructions to use barozp/Qwen3.8-27B-Opus-Distill-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use barozp/Qwen3.8-27B-Opus-Distill-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M

Use Docker

docker model run hf.co/barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use barozp/Qwen3.8-27B-Opus-Distill-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "barozp/Qwen3.8-27B-Opus-Distill-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "barozp/Qwen3.8-27B-Opus-Distill-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M

Ollama
How to use barozp/Qwen3.8-27B-Opus-Distill-GGUF with Ollama:
```
ollama run hf.co/barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M
```

Unsloth Studio

How to use barozp/Qwen3.8-27B-Opus-Distill-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for barozp/Qwen3.8-27B-Opus-Distill-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for barozp/Qwen3.8-27B-Opus-Distill-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for barozp/Qwen3.8-27B-Opus-Distill-GGUF to start chatting

How to use barozp/Qwen3.8-27B-Opus-Distill-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use barozp/Qwen3.8-27B-Opus-Distill-GGUF with Docker Model Runner:
```
docker model run hf.co/barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M
```

Lemonade

How to use barozp/Qwen3.8-27B-Opus-Distill-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-27B-Opus-Distill-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use barozp/Qwen3.8-27B-Opus-Distill-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use barozp/Qwen3.8-27B-Opus-Distill-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "barozp/Qwen3.8-27B-Opus-Distill-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3.8-27B-Opus-Distill-GGUF / README.md

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	---
	license: apache-2.0
	base_model: barozp/Qwen3.8-27B-Opus-Distill
	base_model_relation: quantized
	library_name: llama.cpp
	pipeline_tag: image-text-to-text
	tags:
	- gguf
	- llama.cpp
	- qwen
	- reasoning
	- opus-distill
	- vision
	- mtp
	- imatrix
	- quantized
	---

	# Qwen3.8-27B-Opus-Distill-GGUF

	GGUF quantizations of
	[barozp/Qwen3.8-27B-Opus-Distill](https://huggingface.co/barozp/Qwen3.8-27B-Opus-Distill) —
	a Qwen3.8-27B fine-tuned with LoRA on Claude Opus reasoning traces (merged),
	with the native vision tower and native MTP head carried over untouched.

	## Highlights

	- Reasoning-distilled, not just quantized. The LoRA was trained on 14,250
	Opus chain-of-thought traces and merged into the base weights. Quantization
	only converts the weights — the reasoning gains travel with them unchanged.
	- Full multimodal. Native vision tower ships as a separate `mmproj` file
	(~0.9 GB). Text-only users can ignore it entirely.
	- Native MTP for self-speculative decoding. The model was released with its
	MTP head trained in — unlike grafted MTP setups, no approximation involved.
	Free speedups on compute-bound hardware.
	- imatrix-calibrated. All quants below Q3_K_M use an importance matrix
	built from the model's own reasoning-distillation data (see [Imatrix](#imatrix)).

	## Known issues

	Reasoning loop under stacked output-format constraints. Reported by
	[zxbc2023](https://huggingface.co/zxbc2023) ([full writeup, discussion #1](https://huggingface.co/barozp/Qwen3.8-27B-Opus-Distill/discussions/1)).
	Combining `"no prose"` with a second output-format constraint (e.g. `"no
	markdown"` or `"no comments"`) can send this model into a non-converging
	self-verification reasoning loop -- it burns the entire token budget with
	zero visible output. Fully deterministic and reproducible at temp=0.
	Root cause: traced to part of the training data being sourced from
	reconstructed (not verbatim) Opus reasoning traces, not a capability gap.

	Fixed in [barozp/Qwen3.8-27B-Opus-Distill-v2-clean](https://huggingface.co/barozp/Qwen3.8-27B-Opus-Distill-v2-clean)
	-- retrained on a rebuilt dataset where every row is traced to a verified
	genuine source. If you're hitting this, switch to v2-clean.

	Workaround if staying on this version: avoid combining `"no prose"` with
	another format constraint, or raise the generation token budget to >=4096
	for constrained code-gen tasks.

	## Quality benchmarks (of the source safetensors model)

	Measured with `lm-evaluation-harness`: **0-shot, loglikelihood (multiple-choice),
	chat template OFF, QUICK mode (`--limit 500`)**. Base and distill ran with the
	identical harness, so the Δ column is the meaningful signal.

	\| Task \| Metric \| Base \| Distill \| Δ \|
	\|---\|---\|---:\|---:\|---:\|
	\| wikitext \| word perplexity ↓ \| 8.434 \| 8.344 \| −0.09 \|
	\| mmlu \| acc \| 0.849 \| 0.849 \| −0.001 \|
	\| hellaswag \| acc_norm \| 0.742 \| 0.740 \| −0.002 \|
	\| arc_challenge \| acc_norm \| 0.588 \| 0.630 \| +0.042 \|
	\| gpqa_diamond \| acc_norm \| 0.232 \| 0.495 \| +0.263 \|

	Reading the table:

	- Reasoning improved (ARC +4.2pt, GPQA +26pt), knowledge stayed flat
	(MMLU −0.001) and language modeling stayed flat (wikitext −0.09 ppl).
	- GPQA caveat: measured with thinking disabled (loglikelihood) — the base
	scores near random (25%) because it gets no chance to deliberate. The +26pt Δ
	is a valid same-protocol comparison, but do not compare 0.495 to Qwen's
	published 89.2 (measured with thinking ON, different harness).
	- ARC-Challenge is saturated for modern models; treat it as continuity with
	the Qwen3.6 release — GPQA is the stronger reasoning signal here.

	## Speed (MTP self-speculative decoding)

	Not yet benchmarked for this exact model. On the Qwen3.6 sibling (same MTP
	mechanism, grafted there), measured with llama.cpp: **+39% tok/s full offload,
	+67% partial offload** with spec-decode ON. Native MTP (this model) is trained
	in and typically does at least as well. Guidance:

	- Compute-bound (full offload, strong GPU) → enable `--spec-type draft-mtp`.
	- Memory-bandwidth-bound (partial offload) → keep spec off.

	## Available quantizations

	\| File \| Size \| Bits/w \| Use case \|
	\|---\|---:\|---:\|---\|
	\| `Qwen3.8-27B-Opus-Distill-BF16.gguf` \| 54.7 GB \| 16.0 \| reference / re-quantization source \|
	\| `Qwen3.8-27B-Opus-Distill-Q8_0.gguf` \| 29.0 GB \| 8.5 \| near-lossless \|
	\| `Qwen3.8-27B-Opus-Distill-Q6_K.gguf` \| 22.4 GB \| 6.6 \| high quality \|
	\| `Qwen3.8-27B-Opus-Distill-Q5_K_M.gguf` \| 19.5 GB \| 5.7 \| quality / balanced \|
	\| `Qwen3.8-27B-Opus-Distill-Q4_K_M.gguf` \| 16.8 GB \| 4.9 \| recommended all-rounder \|
	\| `Qwen3.8-27B-Opus-Distill-Q3_K_M.gguf` \| 13.5 GB \| 4.0 \| tight VRAM \|
	\| `Qwen3.8-27B-Opus-Distill-IQ3_XXS.gguf` \| 11.4 GB \| 3.3 \| low-bit, imatrix \|
	\| `Qwen3.8-27B-Opus-Distill-IQ2_XXS.gguf` \| 8.7 GB \| 2.5 \| very low-bit, imatrix \|
	\| `Qwen3.8-27B-Opus-Distill-IQ1_M.gguf` \| 7.9 GB \| 2.3 \| extreme low-bit, imatrix \|

	K-quants (`Q8_0`–`Q3_K_M`) are plain `llama-quantize` passes, no imatrix needed.
	IQ-quants (`IQ3_XXS` and below) require an importance matrix to run at all
	in current `llama.cpp` and are built from the one in this repo (see below).

	Which one to pick:

	- Best quality with headroom → Q6_K or Q8_0
	- Best quality/size balance → Q4_K_M (default recommendation)
	- 24 GB card → Q4_K_M; 16 GB card → Q3_K_M (partial offload)
	- Below that → IQ quants, accept the quality hit

	## Imatrix

	`imatrix.dat` in this repo (512 samples from
	[barozp/opus-reasoning-distill-train](https://huggingface.co/datasets/barozp/opus-reasoning-distill-train),
	context 512) was used to build the IQ quants above. It applies to any GGUF with
	this same architecture — including the base
	[Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) — so it can be reused
	for re-quantization without recomputing it:

	```bash
	llama-quantize --imatrix imatrix.dat model-BF16.gguf model-IQ4_XS.gguf IQ4_XS
	```

	Note on `IQ1_M`: the MTP head (`blk.64`, the `nextn.*` decoder layer) is
	never exercised by a normal forward pass, so the imatrix has no data for it.
	`llama-quantize` pins that block to `q4_K` instead of failing, which is why
	`IQ1_M` lands at ~2.3 bits/weight (7.9 GB) rather than the ~1.8 a "pure"
	IQ1_M would suggest — the MTP head alone accounts for the difference, the rest
	of the model is quantized normally.

	## Vision (mmproj)

	The vision tower is in `Qwen3.8-27B-Opus-Distill-mmproj-f16.gguf` (~0.9 GB) in
	this repo. Load it alongside any quant for image/video input:

	```bash
	llama-server -m Qwen3.8-27B-Opus-Distill-Q4_K_M.gguf --mmproj Qwen3.8-27B-Opus-Distill-mmproj-f16.gguf
	```

	Text-only usage does not need mmproj and runs fine without it.

	## Quick start

	```bash
	# build llama.cpp with CUDA, then:

	# text-only chat
	llama-cli -m Qwen3.8-27B-Opus-Distill-Q4_K_M.gguf -no-cnv

	# multimodal server
	llama-server -m Qwen3.8-27B-Opus-Distill-Q4_K_M.gguf --mmproj Qwen3.8-27B-Opus-Distill-mmproj-f16.gguf

	# with self-speculative decoding (compute-bound hardware)
	llama-cli -m Qwen3.8-27B-Opus-Distill-Q4_K_M.gguf -no-cnv --spec-type draft-mtp -fa on
	```

	## Training details (source safetensors model)

	- Base: Qwen/Qwen3.8-27B — dense 27B, hybrid Gated-DeltaNet / full-attention, 64 layers
	- Method: LoRA r=64, alpha=64, dropout 0.05, merged into base weights
	- LoRA targets: attention q/k/v/o_proj on the 16 full-attention layers; FFN
	gate/up/down_proj on all 64 layers (Gated-DeltaNet projections untouched)
	- Data: barozp/opus-reasoning-distill-train (14,250) + -validation (750, held out)
	- Run: 1 epoch (891 steps), lr 1e-4 cosine + 3% warmup, effective batch 16,
	MAX_SEQ 4096, bf16, ~5h52m on A100 80GB
	- Final validation loss: 0.4647
	- Vision + MTP: carried over byte-for-byte from the base checkpoint — never trained

	## Notes

	- Thinking mode is on by default (same as the base model). The GGUF embeds
	the chat template; how thinking is toggled depends on the llama.cpp version /
	frontend (e.g., LM Studio exposes the setting in its UI).
	- Conversion: llama.cpp `convert_hf_to_gguf.py` from the corrected multimodal
	config (nested `text_config` + `vision_config`).
	- No chaining: every quant was produced directly from the BF16 GGUF, so
	errors do not accumulate across the ladder.

	## Source chain

	[Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) (base)
	→ [barozp/Qwen3.8-27B-Opus-Distill](https://huggingface.co/barozp/Qwen3.8-27B-Opus-Distill) (LoRA finetune, safetensors)
	→ this repo (GGUF quantizations)

	See [Known Issues](#known-issues) above -- if you're hitting the reasoning-loop bug, [barozp/Qwen3.8-27B-Opus-Distill-v2-clean-GGUF](https://huggingface.co/barozp/Qwen3.8-27B-Opus-Distill-v2-clean-GGUF) fixes it.