Instructions to use rchow93/crewai-qwen3-8b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rchow93/crewai-qwen3-8b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rchow93/crewai-qwen3-8b-gguf",
	filename="crewai-qwen3-8b-thinking-v2-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use rchow93/crewai-qwen3-8b-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 rchow93/crewai-qwen3-8b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rchow93/crewai-qwen3-8b-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 rchow93/crewai-qwen3-8b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rchow93/crewai-qwen3-8b-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 rchow93/crewai-qwen3-8b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rchow93/crewai-qwen3-8b-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 rchow93/crewai-qwen3-8b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rchow93/crewai-qwen3-8b-gguf:Q4_K_M

Use Docker

docker model run hf.co/rchow93/crewai-qwen3-8b-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use rchow93/crewai-qwen3-8b-gguf with Ollama:
```
ollama run hf.co/rchow93/crewai-qwen3-8b-gguf:Q4_K_M
```

Unsloth Studio

How to use rchow93/crewai-qwen3-8b-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 rchow93/crewai-qwen3-8b-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 rchow93/crewai-qwen3-8b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rchow93/crewai-qwen3-8b-gguf to start chatting

How to use rchow93/crewai-qwen3-8b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf rchow93/crewai-qwen3-8b-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": "rchow93/crewai-qwen3-8b-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use rchow93/crewai-qwen3-8b-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 rchow93/crewai-qwen3-8b-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 rchow93/crewai-qwen3-8b-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use rchow93/crewai-qwen3-8b-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf rchow93/crewai-qwen3-8b-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 "rchow93/crewai-qwen3-8b-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 rchow93/crewai-qwen3-8b-gguf with Docker Model Runner:
```
docker model run hf.co/rchow93/crewai-qwen3-8b-gguf:Q4_K_M
```

Lemonade

How to use rchow93/crewai-qwen3-8b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rchow93/crewai-qwen3-8b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.crewai-qwen3-8b-gguf-Q4_K_M

List all available models

lemonade list

CrewAI Code Generation - Qwen3 8B (GGUF)

Fine-tuned Qwen3-8B models for generating CrewAI multi-agent code from natural language descriptions.

Models Available

V2 Thinking Models (Recommended)

File	Size	Description
crewai-qwen3-8b-thinking-v2-q4_k_m.gguf	4.7 GB	4-bit quantized, best balance
crewai-qwen3-8b-thinking-v2-q8_0.gguf	8.2 GB	8-bit quantized, higher quality

V2 Improvements:

Native Qwen3 chat template (ChatML format)
Higher LoRA rank (r=32 vs r=16)
Thinking mode with reasoning tags
Better structured outputs

V1 Models (Legacy)

File	Size	Description
qwen3-8b.Q4_K_M.gguf	~4.7 GB	4-bit quantized
qwen3-8b.Q5_K_M.gguf	~5.5 GB	5-bit quantized
qwen3-8b.Q8_0.gguf	~8.2 GB	8-bit quantized

Usage with Ollama

Download the GGUF file
Create a Modelfile:

FROM ./crewai-qwen3-8b-thinking-v2-q4_k_m.gguf

TEMPLATE """{{- if .System }}
<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""

SYSTEM """You are a CrewAI code generation expert. When given a task description and required inputs, generate complete, working CrewAI Python code that includes:
- All necessary imports (crewai, crewai_tools)
- Agent definitions with roles, goals, backstories, and tools
- Task definitions with proper context dependencies
- Crew instantiation with appropriate process type
- Kickoff code with the provided inputs

Think through the problem step by step before generating code."""

PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER num_ctx 8192
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"

Create and run:

ollama create crewai-qwen3-8b -f Modelfile
ollama run crewai-qwen3-8b

Example Prompt

Create a CrewAI crew for analyzing competitor websites.
Required inputs:
- competitor_urls: list of URLs to analyze
- analysis_focus: what aspects to focus on

Training Details

Base Model: Qwen/Qwen3-8B
Fine-tuning: Unsloth + QLoRA (r=32, alpha=32)
Dataset: 2,500 CrewAI code examples
Training: 3 epochs on RTX 4090

License

Apache 2.0 (same as base Qwen3 model)

Downloads last month: 7

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

4-bit

5-bit

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for rchow93/crewai-qwen3-8b-gguf

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Quantized

(344)

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