Instructions to use KarLexAI/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 KarLexAI/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 KarLexAI/Qwen3.8-27B-GGUF:IQ1_S
# Run inference directly in the terminal:
llama cli -hf KarLexAI/Qwen3.8-27B-GGUF:IQ1_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KarLexAI/Qwen3.8-27B-GGUF:IQ1_S
# Run inference directly in the terminal:
llama cli -hf KarLexAI/Qwen3.8-27B-GGUF:IQ1_S

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf KarLexAI/Qwen3.8-27B-GGUF:IQ1_S
# Run inference directly in the terminal:
./build/bin/llama cli -hf KarLexAI/Qwen3.8-27B-GGUF:IQ1_S

Use Docker

docker model run hf.co/KarLexAI/Qwen3.8-27B-GGUF:IQ1_S

Llama new
LM Studio
Jan

vLLM

How to use KarLexAI/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 "KarLexAI/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": "KarLexAI/Qwen3.8-27B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KarLexAI/Qwen3.8-27B-GGUF:IQ1_S

Ollama
How to use KarLexAI/Qwen3.8-27B-GGUF with Ollama:
```
ollama run hf.co/KarLexAI/Qwen3.8-27B-GGUF:IQ1_S
```
Unsloth Desktop

How to use KarLexAI/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 KarLexAI/Qwen3.8-27B-GGUF:IQ1_S

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "KarLexAI/Qwen3.8-27B-GGUF:IQ1_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use KarLexAI/Qwen3.8-27B-GGUF with Docker Model Runner:
```
docker model run hf.co/KarLexAI/Qwen3.8-27B-GGUF:IQ1_S
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KarLexAI/Qwen3.8-27B-GGUF:IQ1_S

Run and chat with the model

lemonade run user.Qwen3.8-27B-GGUF-IQ1_S

List all available models

lemonade list

Hermes Agent

How to use KarLexAI/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 KarLexAI/Qwen3.8-27B-GGUF:IQ1_S

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 KarLexAI/Qwen3.8-27B-GGUF:IQ1_S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use KarLexAI/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 KarLexAI/Qwen3.8-27B-GGUF:IQ1_S

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 "KarLexAI/Qwen3.8-27B-GGUF:IQ1_S" \
  --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 · KLX GGUF

27B reasoning from 6.2 GB. It still thinks.

The smallest GGUFs of a 27B reasoning model usually stop reasoning: they loop, run out of tokens and miss easy problems. KLX files keep more of the model at every size. At 6.2 GB, the KLX file solves 53.8% of AIME 2025+2026; Unsloth's file of the same size solves 1.3%. Standard GGUF, no fine-tuning, no custom runtime.

✨ Highlights

🧠 53.8% on AIME 2025+2026 and 87.8% on MATH-500 from a 6.19 GB file. Unsloth's 6.19 GB file scores 1.3% and 32.6%.
📈 At 7.2 GB, 8.75 more AIME answers right in every 100 than Unsloth's file of the same size.
🎯 Near full quality from 10.9 GB. 94.2% AIME, 94.3% IFEval and 99.0% MATH-500, against 96.0%, 93.4% and 99.2% for full BF16 (about 54 GB).
🔌 Plain GGUF. Only standard llama.cpp tensor types: nothing to patch, no fork to build.

📦 Pick your file

File	Size	AIME 2025+2026	IFEval	MATH-500	Pick it for
Qwen3.8-27B-KLX-IQ1_S.gguf	6.19 GB	53.8	50.5	87.8	the smallest download
Qwen3.8-27B-KLX-IQ2_XXS.gguf	7.21 GB	82.7	88.5	98.4	the biggest step up per GB
Qwen3.8-27B-KLX-IQ2_XS.gguf	8.00 GB	89.4	93.5	97.8	strong reasoning at 8 GB
Qwen3.8-27B-KLX-IQ3_XXS.gguf	10.93 GB	94.2	94.3	99.0	near full quality
Qwen3.8-27B-KLX-IQ4_XS.gguf	14.25 GB	95.2	94.3	98.8	the closest to full quality
Qwen3.8-27B BF16, for reference	about 54 GB	96.0	93.4	99.2

The file names give the size class. Each file mixes several standard tensor types.

🥊 Head to head

Same harness, same questions, paired differences in points with one standard error.

Size	KLX file	Against	AIME 2025+2026	IFEval	MATH-500
6.2 GB	IQ1_S	Unsloth UD-IQ1_S (6.19 GB)	+52.5 ± 4.9	+28.8 ± 2.6	+55.2 ± 2.5
7.2 GB	IQ2_XXS	Unsloth UD-IQ2_XXS (7.27 GB)	+8.75 ± 2.6	+8.3 ± 1.9	+1.8 ± 0.9
8 GB	IQ2_XS	ISTA GSQ-RCO IQ2_XS (8.42 GB)	+0.2 ± 1.5	-1.1 ± 1.1	+0.2 ± 0.8
8 GB	IQ2_XS	Unsloth UD-IQ2_S (8.37 GB)	+1.0 ± 1.4	+2.8 ± 1.3	-0.8 ± 0.8
10.9 GB	IQ3_XXS	Unsloth UD-IQ3_XXS (10.93 GB)	+1.0 ± 1.2	-0.4 ± 0.8	+0.2 ± 0.4
14.3 GB	IQ4_XS	Unsloth UD-IQ4_XS (14.25 GB)	-0.6 ± 0.9	-1.1 ± 0.8	+0.2 ± 0.5

From 10.9 GB up, both makers' files are close to full quality, and the benchmarks above cannot separate them. Setup, every score and the file hashes: BENCHMARKS.md.

🚀 Run it with two commands

hf download KarLexAI/Qwen3.8-27B-GGUF Qwen3.8-27B-KLX-IQ1_S.gguf --local-dir .
llama-server -m Qwen3.8-27B-KLX-IQ1_S.gguf -ngl 99 -c 32768 --jinja

Then open http://localhost:8080 and chat. Thinking is on by default, and Qwen's sampling settings (temperature 1.0, top-k 20, top-p 0.95) are stored in the file.

🧾 The fine print

Post-training quantization of Qwen/Qwen3.8-27B. No fine-tuning. The architecture, tokenizer and chat template are unchanged; the multi-token prediction (MTP) head is not included.
Every number above comes from one harness on stock llama.cpp, in thinking mode with Qwen's sampler.
Apache-2.0, like the base model. See NOTICE.

Made by KarLex AI. If these files save you memory, a ❤️ on this repo helps others find them.

_{Not affiliated with Qwen, Unsloth or ISTA.}

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GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

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

Base model

Qwen/Qwen3.8-27B

Quantized

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this model