Instructions to use aj9o9/Qwen3.8-27B-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 aj9o9/Qwen3.8-27B-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 aj9o9/Qwen3.8-27B-GGUF
# Run inference directly in the terminal:
llama cli -hf aj9o9/Qwen3.8-27B-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aj9o9/Qwen3.8-27B-GGUF
# Run inference directly in the terminal:
llama cli -hf aj9o9/Qwen3.8-27B-GGUF

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 aj9o9/Qwen3.8-27B-GGUF
# Run inference directly in the terminal:
./llama-cli -hf aj9o9/Qwen3.8-27B-GGUF

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 aj9o9/Qwen3.8-27B-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aj9o9/Qwen3.8-27B-GGUF

Use Docker

docker model run hf.co/aj9o9/Qwen3.8-27B-GGUF

LM Studio
Jan

vLLM

How to use aj9o9/Qwen3.8-27B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aj9o9/Qwen3.8-27B-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": "aj9o9/Qwen3.8-27B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aj9o9/Qwen3.8-27B-GGUF

Ollama
How to use aj9o9/Qwen3.8-27B-GGUF with Ollama:
```
ollama run hf.co/aj9o9/Qwen3.8-27B-GGUF
```

Unsloth Studio

How to use aj9o9/Qwen3.8-27B-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 aj9o9/Qwen3.8-27B-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 aj9o9/Qwen3.8-27B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for aj9o9/Qwen3.8-27B-GGUF to start chatting

How to use aj9o9/Qwen3.8-27B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aj9o9/Qwen3.8-27B-GGUF

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": "aj9o9/Qwen3.8-27B-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use aj9o9/Qwen3.8-27B-GGUF with Docker Model Runner:
```
docker model run hf.co/aj9o9/Qwen3.8-27B-GGUF
```

Lemonade

How to use aj9o9/Qwen3.8-27B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aj9o9/Qwen3.8-27B-GGUF

Run and chat with the model

lemonade run user.Qwen3.8-27B-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use aj9o9/Qwen3.8-27B-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 aj9o9/Qwen3.8-27B-GGUF

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 aj9o9/Qwen3.8-27B-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use aj9o9/Qwen3.8-27B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aj9o9/Qwen3.8-27B-GGUF

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 "aj9o9/Qwen3.8-27B-GGUF" \
  --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 GGUF

GGUF of Qwen/Qwen3.8-27B for llama.cpp. More files will land here. First one is a role mix I baked for my 3090, not a flat IQ3.

A note from me

Same deal as my Ling-3.0-flash and Nemotron-3.5-Lightning uploads: I make these to run on my own box, then share them.

Please report anything you find. Bad output, crashes, wrong metadata, a better flag for a particular card — open a discussion here or reach me at @ItsmeAjayKV.

Files

File	Size	What it is	Status
`Qwen3.8-27B-gdn8-q6attn-iq3ffn.gguf`	13.6 GB (12.7 GiB) / 3.99 bpw	Role mix. GDN state Q8, attn Q6, mid-FFN IQ3_XXS. See below.	up
`Qwen3.8-27B-Q4_K_M.gguf`	~16–18 GB	Flat K-quant, if I bake one from the same BF16	waiting
`Qwen3.8-27B-Q5_K_M.gguf`	~19–20 GB	Same	waiting
`Qwen3.8-27B-imatrix-v6.gguf`	~14 MB	Imatrix used for the mix. Reusable.	maybe

This is not a flat IQ3_XXS. Only the mid-FFN tensors are IQ3_XXS. The filename is the recipe.

Download (CLI)

hf download hf://aj9o9/Qwen3.8-27B-GGUF/Qwen3.8-27B-gdn8-q6attn-iq3ffn.gguf

That pulls just the mix, into the current directory. Same thing, older-style:

hf download aj9o9/Qwen3.8-27B-GGUF --include "Qwen3.8-27B-gdn8-q6attn-iq3ffn.gguf"

Imatrix (only if you want to requant, not needed to run):

hf download hf://aj9o9/Qwen3.8-27B-GGUF/Qwen3.8-27B-imatrix-v6.gguf

Rule of thumb I actually use: pick the largest quant that fits in RAM/VRAM, not the largest one you can download.

Should you use the mix?

Take this if you want ~13G and you care more about keeping attention / GDN state fat than a uniform 3-bit file.

Skip this if you can hold Unsloth UD-Q3_K_XL (13.4G) or a Q4. Those beat it on every number I ran. I am not going to pretend otherwise.

What the mix actually is

Official BF16 → my my-mix.txt + bartowski's Qwen3.8 calibration-v6.

Role	Type
GDN state (`ssm_*` except out)	Q8_0
Full attention Q/K/V/O, embed, output, MTP	Q6_K
GDN mixers (`attn_qkv`, `attn_gate`, `ssm_out`)	Q4_K
FFN edge (layers 0–3, 60–63)	IQ3_S
FFN mid (everything else `ffn_*`)	IQ3_XXS
norms	F32

Imatrix: bartowski calibration-v6, rendered through this model's chat template. 583 chunks at -c 512, --parse-special --process-output. About 63% of that file is tool-call text. I did not use wiki-only calib.

Needs a recent llama.cpp with qwen35. Old trees will not load it.

Numbers I actually measured

Same box, same llama.cpp, same prompts. Wiki KLD is vs official BF16 logits (wiki.test.raw, n_ctx=512). GLSL / hard is my locked suite, think off, t=0.

model	size	wiki KLD ↓	GLSL ↑	hard
Unsloth Q4_K_M	16G	0.015	0.946	6/6
Unsloth UD-Q3_K_XL	13.4G	0.031	0.922	6/6
bartowski Q3_K_S	13.7G	0.070	0.892	5/6
this mix	13.6 GB	0.073	0.863	5/6
Unsloth Q5_K_M	19G	0.006	0.855	5/6
bartowski IQ3_M	13.9G	0.057	0.831	5/6

Read it like this:

If you have 16G+, take Q4.
If you want the best 13G-class file I measured, take Unsloth UD-Q3_K_XL, not mine.
This mix beats bartowski IQ3_M on GLSL (0.863 vs 0.831) at a slightly smaller size, and it beats Unsloth Q5 on GLSL at 6G less. That is the honest reason it exists.
Hard: I fail lfu_cache. Q4 and UD-Q3 pass all six. I am not going to hide that.
Tools (single-shot + short agent loops) were a tie. Everyone passed the easy set. I will not claim a tools win.

Wiki KLD is English Wikipedia. The imatrix is chat + tools. Those two will not rank the same, and they didn't.

How I run it

llama-server \
  -m Qwen3.8-27B-gdn8-q6attn-iq3ffn.gguf \
  --host 127.0.0.1 --port 8080 \
  -ngl 999 -fa on --jinja \
  -np 1 -t 12 \
  --alias qwen38-27b-gdn8 \
  --cache-type-k q8_0 --cache-type-v q8_0 \
  --spec-type draft-mtp \
  -c 24576

From the Hub:

llama-server \
  --hf-repo aj9o9/Qwen3.8-27B-GGUF \
  --hf-file Qwen3.8-27B-gdn8-q6attn-iq3ffn.gguf \
  -ngl 999 -fa on --jinja \
  --spec-type draft-mtp \
  --cache-type-k q8_0 --cache-type-v q8_0 \
  -c 24576

MTP tensors are in the file (Q6_K). --spec-type draft-mtp is optional; it drafts, it does not change quality.

Official sampling from the Qwen card:

Thinking: temperature=1.0, top_p=0.95, top_k=20
Instruct / no-think: temperature=0.7, top_p=0.80, top_k=20, presence_penalty=1.5

I graded the table above at t=0, think off, so I could actually compare quants.

Hardware this was made on


GPU	RTX 3090 24 GB
System RAM	64 GB
Runtime	llama.cpp `master`, arch `qwen35`

The mix is meant to leave room for context on a 24 GB card. A 16 GB card can load the weights; keep -c honest.

Vision: this GGUF is text weights only. If you want the encoder, grab an mmproj from the official convert or from bartowski/unsloth and pass --mmproj. I have not tested that pairing.

About the model

27B dense, hybrid 16 × (3 GDN + 1 full attn), 64 layers + MTP
hidden 5120, native 262k context
thinking on by default in the official template

See Qwen/Qwen3.8-27B for the real model card. Their numbers are BF16, not this file.

License

Apache 2.0, same as Qwen/Qwen3.8-27B. LICENSE is in this repo.

Disclaimer

Not affiliated with Alibaba, Qwen, Unsloth, or bartowski. Provided as-is. Use the official card for intended use, safety, and limitations.

Downloads last month: 567

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for aj9o9/Qwen3.8-27B-GGUF

Base model

Qwen/Qwen3.8-27B

Quantized

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this model

aj9o9
/

Qwen3.8-27B-GGUF

Qwen3.8-27B GGUF

A note from me

Files

Download (CLI)

Should you use the mix?

What the mix actually is

Numbers I actually measured

How I run it

Hardware this was made on

About the model

Links

License

Disclaimer

Model tree for aj9o9/Qwen3.8-27B-GGUF