Instructions to use evalstate/qwen-capybara-medium-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 evalstate/qwen-capybara-medium-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 evalstate/qwen-capybara-medium-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf evalstate/qwen-capybara-medium-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 evalstate/qwen-capybara-medium-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf evalstate/qwen-capybara-medium-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 evalstate/qwen-capybara-medium-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf evalstate/qwen-capybara-medium-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 evalstate/qwen-capybara-medium-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf evalstate/qwen-capybara-medium-gguf:Q4_K_M

Use Docker

docker model run hf.co/evalstate/qwen-capybara-medium-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use evalstate/qwen-capybara-medium-gguf with Ollama:
```
ollama run hf.co/evalstate/qwen-capybara-medium-gguf:Q4_K_M
```

Unsloth Studio

How to use evalstate/qwen-capybara-medium-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 evalstate/qwen-capybara-medium-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 evalstate/qwen-capybara-medium-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for evalstate/qwen-capybara-medium-gguf to start chatting

How to use evalstate/qwen-capybara-medium-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf evalstate/qwen-capybara-medium-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": "evalstate/qwen-capybara-medium-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use evalstate/qwen-capybara-medium-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf evalstate/qwen-capybara-medium-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 "evalstate/qwen-capybara-medium-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 evalstate/qwen-capybara-medium-gguf with Docker Model Runner:
```
docker model run hf.co/evalstate/qwen-capybara-medium-gguf:Q4_K_M
```

Lemonade

How to use evalstate/qwen-capybara-medium-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull evalstate/qwen-capybara-medium-gguf:Q4_K_M

Run and chat with the model

lemonade run user.qwen-capybara-medium-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use evalstate/qwen-capybara-medium-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 evalstate/qwen-capybara-medium-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 evalstate/qwen-capybara-medium-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

qwen-capybara-medium-gguf

This is a GGUF conversion of evalstate/qwen-capybara-medium, which is a LoRA fine-tuned version of Qwen/Qwen2.5-0.5B.

Model Details

Base Model: Qwen/Qwen2.5-0.5B
Fine-tuned Model: evalstate/qwen-capybara-medium
Training: Supervised Fine-Tuning (SFT) with TRL
Format: GGUF (for llama.cpp, Ollama, LM Studio, etc.)

Available Quantizations

File	Quant	Size	Description	Use Case
qwen-capybara-medium-f16.gguf	F16	~1GB	Full precision	Best quality, slower
qwen-capybara-medium-q8_0.gguf	Q8_0	~500MB	8-bit	High quality
qwen-capybara-medium-q5_k_m.gguf	Q5_K_M	~350MB	5-bit medium	Good quality, smaller
qwen-capybara-medium-q4_k_m.gguf	Q4_K_M	~300MB	4-bit medium	Recommended - good balance

Usage

With llama.cpp

# Download model
huggingface-cli download evalstate/qwen-capybara-medium-gguf qwen-capybara-medium-q4_k_m.gguf

# Run with llama.cpp
./llama-cli -m qwen-capybara-medium-q4_k_m.gguf -p "Your prompt here"

With Ollama

Create a Modelfile:

FROM ./qwen-capybara-medium-q4_k_m.gguf

Create the model:

ollama create qwen-capybara -f Modelfile
ollama run qwen-capybara

With LM Studio

Download the .gguf file
Import into LM Studio
Start chatting!

Training Details

This model was fine-tuned using:

Dataset: trl-lib/Capybara (1,000 examples)
Method: Supervised Fine-Tuning with LoRA
Epochs: 3
LoRA rank: 16
Hardware: A10G Large GPU

License

Inherits the license from the base model: Qwen/Qwen2.5-0.5B

Citation

@misc{qwen-capybara-medium-gguf,
  author = {evalstate},
  title = {Qwen Capybara Medium GGUF},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/evalstate/qwen-capybara-medium-gguf}
}

Converted to GGUF format using llama.cpp

Downloads last month: 3

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

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Model tree for evalstate/qwen-capybara-medium-gguf

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Model Details

Available Quantizations

Usage

With llama.cpp

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With LM Studio

Training Details

License

Citation

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