Instructions to use Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF",
	filename="PINQWEN-3.5-9B-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Blackfrost-AI/PINQWEN-3.5-9B-1M-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/PINQWEN-3.5-9B-1M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Blackfrost-AI/PINQWEN-3.5-9B-1M-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/PINQWEN-3.5-9B-1M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Blackfrost-AI/PINQWEN-3.5-9B-1M-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/PINQWEN-3.5-9B-1M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Blackfrost-AI/PINQWEN-3.5-9B-1M-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/PINQWEN-3.5-9B-1M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Blackfrost-AI/PINQWEN-3.5-9B-1M-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/PINQWEN-3.5-9B-1M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF:Q4_K_M

Ollama
How to use Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF with Ollama:
```
ollama run hf.co/Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF:Q4_K_M
```

Unsloth Studio

How to use Blackfrost-AI/PINQWEN-3.5-9B-1M-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/PINQWEN-3.5-9B-1M-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/PINQWEN-3.5-9B-1M-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/PINQWEN-3.5-9B-1M-GGUF to start chatting

How to use Blackfrost-AI/PINQWEN-3.5-9B-1M-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/PINQWEN-3.5-9B-1M-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": "Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Blackfrost-AI/PINQWEN-3.5-9B-1M-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/PINQWEN-3.5-9B-1M-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/PINQWEN-3.5-9B-1M-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Blackfrost-AI/PINQWEN-3.5-9B-1M-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/PINQWEN-3.5-9B-1M-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/PINQWEN-3.5-9B-1M-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"

Docker Model Runner
How to use Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF with Docker Model Runner:
```
docker model run hf.co/Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF:Q4_K_M
```

Lemonade

How to use Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.PINQWEN-3.5-9B-1M-GGUF-Q4_K_M

List all available models

lemonade list

PINQWEN‑3.5‑9B‑1M — GGUF

🪟 A 9B that reads a million tokens, thinks before it speaks, and never refuses — run it on your own machine.

PINQWEN‑3.5‑9B‑1M is Blackfrost AI's compact powerhouse — a reasoning model distilled through The Void, our multi‑teacher reasoning‑distillation method, on the Qwen 3.5 9B architecture. Reasons first, answers second (<think> → answer), across a full one‑million‑token context window. This is the complete GGUF drop for llama.cpp — every quant, the vision projector, and the MTP speculative‑decode head for faster generation.

🚀 Why PINQWEN‑3.5‑9B‑1M?

🪟 1,000,000‑token context — whole codebases and books in one prompt, YaRN‑extended for efficient long context.
🔓 Uncensored — answers directly, no reflexive refusals.
🧠 Reasons in the open — native <think>…</think> chain‑of‑thought from a panel of frontier teachers.
⚡ MTP speculative decode — ships with the multi‑token‑prediction head for accelerated generation.
👁 Vision projector included (mmproj-*) for image input in compatible runtimes.

📦 This repository — GGUF: pick your quant

File	Precision	Size	Notes
`PINQWEN-3.5-9B-Q2_K.gguf`	2‑bit	~3.7 GB	Smallest; tight budgets
`PINQWEN-3.5-9B-Q3_K_M.gguf`	3‑bit	~4.5 GB
`PINQWEN-3.5-9B-Q4_K_M.gguf`	4‑bit	~5.4 GB	⭐ Recommended balance
`PINQWEN-3.5-9B-Q5_K_M.gguf`	5‑bit	~6.2 GB	Higher quality
`PINQWEN-3.5-9B-Q6_K.gguf`	6‑bit	~7.1 GB
`PINQWEN-3.5-9B-Q8_0.gguf`	8‑bit	~9.2 GB	Near‑lossless
`PINQWEN-3.5-9B-f16.gguf`	f16	~18 GB	Full precision
`mmproj-PINQWEN-3.5-9B-BF16.gguf`	—	~0.9 GB	Vision projector (image input)

⚡ Quickstart (llama.cpp)

# text
llama-cli -m PINQWEN-3.5-9B-Q4_K_M.gguf -p "Explain the CAP theorem." -ngl 99

# thinking off (direct answers)
llama-server -m PINQWEN-3.5-9B-Q4_K_M.gguf -ngl 99 \
  --chat-template-kwargs '{"enable_thinking":false}'

# with vision
llama-mtmd-cli -m PINQWEN-3.5-9B-Q4_K_M.gguf \
  --mmproj mmproj-PINQWEN-3.5-9B-BF16.gguf --image pic.jpg -p "Describe this."

🎛 Formats — every one is 1M context

Format	Repository	Best for
BF16	`PINQWEN-3.5-9B-1M-BF16`	Reference precision, fine‑tuning, vision
NVFP4	`PINQWEN-3.5-9B-1M-NVFP4`	Fast serving on NVIDIA Blackwell
GGUF	`PINQWEN-3.5-9B-1M-GGUF`	llama.cpp / local — full quant ladder + MTP

📄 License

Apache‑2.0. Base architecture: Qwen 3.5 9B. As an uncensored model, use responsibly.

PINQWEN‑3.5‑9B‑1M — part of Blackfrost AI's Void model family. 🖤

Downloads last month: 329

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

(361)

this model

Collection including Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF

PINQWEN SERIES

Collection

THE QWEN FAMILY FINE-TUNED MODELS IN THE VOID • 5 items • Updated 2 days ago

Blackfrost-AI
/

PINQWEN-3.5-9B-1M-GGUF

PINQWEN‑3.5‑9B‑1M — GGUF

🪟 **A 9B that reads a million tokens, thinks before it speaks, and never refuses — run it on your own machine.**

🚀 Why PINQWEN‑3.5‑9B‑1M?

📦 This repository — GGUF: pick your quant

⚡ Quickstart (llama.cpp)

🎛 Formats — every one is 1M context

📄 License

Model tree for Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF

Collection including Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF

PINQWEN SERIES

PINQWEN‑3.5‑9B‑1M — GGUF

🪟 A 9B that reads a million tokens, thinks before it speaks, and never refuses — run it on your own machine.

🚀 Why PINQWEN‑3.5‑9B‑1M?

📦 This repository — GGUF: pick your quant

⚡ Quickstart (llama.cpp)

🎛 Formats — every one is 1M context

📄 License

Model tree for Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF

Collection including Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF

🪟 **A 9B that reads a million tokens, thinks before it speaks, and never refuses — run it on your own machine.**