Instructions to use Blackfrost-AI/Qwen3.8-27B-ABLITERATED-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 Blackfrost-AI/Qwen3.8-27B-ABLITERATED-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 Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Blackfrost-AI/Qwen3.8-27B-ABLITERATED-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 Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Blackfrost-AI/Qwen3.8-27B-ABLITERATED-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 Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Blackfrost-AI/Qwen3.8-27B-ABLITERATED-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 Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Blackfrost-AI/Qwen3.8-27B-ABLITERATED-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": "Blackfrost-AI/Qwen3.8-27B-ABLITERATED-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/Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF:Q4_K_M

Ollama
How to use Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF with Ollama:
```
ollama run hf.co/Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF:Q4_K_M
```

Unsloth Studio

How to use Blackfrost-AI/Qwen3.8-27B-ABLITERATED-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 Blackfrost-AI/Qwen3.8-27B-ABLITERATED-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 Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF with Pi:

Start the llama.cpp server

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

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": "Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF with Docker Model Runner:
```
docker model run hf.co/Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF:Q4_K_M
```

Lemonade

How to use Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-27B-ABLITERATED-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Blackfrost-AI/Qwen3.8-27B-ABLITERATED-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 Blackfrost-AI/Qwen3.8-27B-ABLITERATED-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 Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Blackfrost-AI/Qwen3.8-27B-ABLITERATED-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 "Blackfrost-AI/Qwen3.8-27B-ABLITERATED-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"

BF16 GGUF

by kapa1 - opened 5 days ago

Discussion

kapa1

5 days ago

Hi, can you post a BF16 GGUF of your Qwen3.8-27B

Blackfrost-AI

Owner 5 days ago

•

edited 5 days ago

Thanks for the request. Unfortunately, I don’t currently have the capacity to host and maintain a separate BF16 GGUF.

The original BF16 checkpoint is already publicly available here:
https://huggingface.co/Blackfrost-AI/Qwen3.8-27B-ABLITERATED-BF16

A BF16 GGUF is a mechanical format conversion of those same weights. The resulting GGUF is approximately 54.6 GB, compared with 55.6 GB for the source repository. On my setup, once the
weights are local, the conversion takes roughly 30 seconds; downloading the weights is normally the time-consuming part.

Hosting another near-identical full-size artifact would not be a responsible use of the project’s paid storage. However, I’ve published complete, agent-ready conversion instructions
here:

https://huggingface.co/Blackfrost-AI/Qwen3.8-27B-ABLITERATED-GGUF/blob/main/GGUF_conversion_agent_instructions.md

You can give that file directly to a coding agent. It includes pinned revisions, the exact BF16 conversion command, disk requirements, metadata and checksum validation, embedded MTP
preservation, a smoke test, and instructions for reusing the existing F16 vision projector.

kapa1

3 days ago

Thanks for the reply, and the link, the safetensors are what I need 😊️ #soulhash.ai

Blackfrost-AI

Owner 3 days ago

Ahhh got you OK cool I’ll get those to you today

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