Instructions to use jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-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 jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-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 jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS

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 jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS

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 jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS

Use Docker

docker model run hf.co/jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS

LM Studio
Jan

vLLM

How to use jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-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": "jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS

Ollama
How to use jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF with Ollama:
```
ollama run hf.co/jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS
```
Unsloth Desktop

How to use jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS

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": "jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF with Docker Model Runner:
```
docker model run hf.co/jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS
```

Lemonade

How to use jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF-IQ4_XS

List all available models

lemonade list

Hermes Agent

How to use jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-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 jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS

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 jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS

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 "jakeatx/ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF:IQ4_XS" \
  --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"

Swift 1.5 Qwen3.8-27B Uncensored — ATX IQ4_XS-M

This is the Swift 1.5 uncensored BF16 checkpoint quantized with the same per-tensor XS-M map as ATX Qwen3.8-27B IQ4_XS-M and the earlier Swift uncensored build. The source derives from UkisAI's Swift 1.5 and retains its MTP head. Source revision: 15165fce17cb716934a2b15e746d9c7b061d4a0f.

The GGUF contains the text model and MTP draft layer. It does not contain the vision tower. This is an uncensored, refusal-reduced derivative; the source author reports structural validation but has not published behavioral or capability measurements for this derivative. No quality or speed benchmark is claimed for this quant.

Artifact	Details
`ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M.gguf`	14.52 GiB (15,588,550,976 bytes), 4.56 bits per weight, SHA-256 `b5c43ac588ef45143afa25441d722ce86d199b77b7d7f808e29a7cffc4f2f244`
`tensor_types_ATX-4-XS.txt`	Exact per-tensor type map, SHA-256 `c1b4daffc5d0f623d259179c375f08dab6b516b1a5d92a64a7e71c4812963268`
`imatrix_swift1_unc_transfer.gguf`	Transferred importance matrix from the earlier Swift 1.0 uncensored quant, SHA-256 `ef52688ab733e4efa16c6486b0941e11883d15434b4553cfa5e6ae64ed3f3ef1`

Quantization recipe

The map uses IQ4_XS for bulk weights, Q5_0 for selected attention output, GDN output, and FFN down weights, Q6_K for the output head and selected K/V projections, Q8_0 for selected K/V projections and small GDN vectors, Q5_0 for the MTP layer, and Q4_K for the token embedding. The output tensor types are checked against the base ATX Qwen3.8-27B quant.

This CPU build uses the earlier Swift uncensored model's importance matrix, originally computed from about 226K calibration tokens at Q8_0 precision. It is a transfer matrix, not a calibration measured on Swift 1.5. That choice keeps CPU load and runtime lower while preserving the exact XS-M tensor strategy; any quality effect of the matrix transfer has not been measured.

llama-quantize --imatrix imatrix_swift1_unc_transfer.gguf \
  --tensor-type-file tensor_types_ATX-4-XS.txt \
  --token-embedding-type q4_K \
  Swift-1.5-Qwen3.8-27B-Uncensored-BF16.gguf \
  ATX-Swift-1.5-Qwen3.8-27B-Uncensored-IQ4_XS-M.gguf iq4_xs 2

The BF16 conversion and quantization used CPU cores 12–13 at low process priority. Source BF16 weights are not redistributed here. The GGUF's SHA-256 is listed above. A structural tensor check was run before upload. The full-file SHA-256 recorded by Hugging Face Xet was checked against the published file after transfer. Inference quality was not re-evaluated.

License and credits

Distributed under the Swift Open License v1.0, inherited from Swift 1.5. See LICENSE, LICENSE-APACHE-2.0, and NOTICE for the applicable terms and attribution. Credit goes to UkisAI for Swift 1.5, d0xin for the uncensored derivative, and the Qwen team for Qwen3.8-27B.