Instructions to use prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/Qwen3-8B-CK-Pro-f32-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 prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16

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 prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16

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 prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16

Use Docker

docker model run hf.co/prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16

LM Studio
Jan

vLLM

How to use prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Qwen3-8B-CK-Pro-f32-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": "prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16

SGLang

How to use prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16
```
Unsloth Desktop

How to use prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16

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": "prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16
```

Lemonade

How to use prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16

Run and chat with the model

lemonade run user.Qwen3-8B-CK-Pro-f32-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/Qwen3-8B-CK-Pro-f32-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 prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16

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 prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16

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 "prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:BF16" \
  --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"

Qwen3-8B-CK-Pro-f32-GGUF

The CognitiveKernel/Qwen3-8B-CK-Pro model is a fine-tuned variant of the Qwen3-8B base language model, trained using self-collected trajectories from queries as detailed in the Cognitive Kernel-Pro research. It is designed as a deep research agent and foundation model, achieving strong performance with Pass@1/3 scores of 32.7%/38.2% on the full GAIA dev set and 40.3%/49.3% on the text-only subset. This model builds upon the strengths of Qwen3-8B, which supports advanced reasoning, instruction-following, and multilingual capabilities, specifically optimized for research agent tasks through the Cognitive Kernel-Pro framework. It is not currently deployed by any inference provider on Hugging Face. The model leverages the underlying Qwen3-8B base and its finetuned versions to deliver enhanced agent capabilities for complex question-answering and information synthesis scenarios.

Execute using Ollama

run ->

ollama run hf.co/prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF:Q2_K

Model Files

File Name	Quant Type	File Size
Qwen3-8B-CK-Pro.BF16.gguf	BF16	16.4 GB
Qwen3-8B-CK-Pro.F16.gguf	F16	16.4 GB
Qwen3-8B-CK-Pro.F32.gguf	F32	32.8 GB
Qwen3-8B-CK-Pro.Q2_K.gguf	Q2_K	3.28 GB

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Downloads last month: 41

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

2-bit

16-bit

32-bit

Model tree for prithivMLmods/Qwen3-8B-CK-Pro-f32-GGUF

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Finetuned

CognitiveKernel/Qwen3-8B-CK-Pro

Quantized

(1)

this model