Instructions to use Bopalv/Qwen3-0.6B-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Bopalv/Qwen3-0.6B-quantized 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 Bopalv/Qwen3-0.6B-quantized:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Bopalv/Qwen3-0.6B-quantized:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Bopalv/Qwen3-0.6B-quantized:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Bopalv/Qwen3-0.6B-quantized:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf Bopalv/Qwen3-0.6B-quantized:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf Bopalv/Qwen3-0.6B-quantized:Q4_K_M

Use Docker

docker model run hf.co/Bopalv/Qwen3-0.6B-quantized:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use Bopalv/Qwen3-0.6B-quantized with Ollama:
```
ollama run hf.co/Bopalv/Qwen3-0.6B-quantized:Q4_K_M
```
Unsloth Desktop

How to use Bopalv/Qwen3-0.6B-quantized with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Bopalv/Qwen3-0.6B-quantized: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": "Bopalv/Qwen3-0.6B-quantized:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Bopalv/Qwen3-0.6B-quantized with Docker Model Runner:
```
docker model run hf.co/Bopalv/Qwen3-0.6B-quantized:Q4_K_M
```

Lemonade

How to use Bopalv/Qwen3-0.6B-quantized with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Bopalv/Qwen3-0.6B-quantized:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-0.6B-quantized-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Bopalv/Qwen3-0.6B-quantized with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Bopalv/Qwen3-0.6B-quantized: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 Bopalv/Qwen3-0.6B-quantized:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Bopalv/Qwen3-0.6B-quantized with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Bopalv/Qwen3-0.6B-quantized: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 "Bopalv/Qwen3-0.6B-quantized: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"

Qwen3-0.6B-quantized / DPO-Training /DPO-Complete-Guide.md

Bopalv

Upload DPO-Training/DPO-Complete-Guide.md with huggingface_hub

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# Qwen3-0.6B DPO Training - Complete Setup

## What's Ready

### 1. Downloaded DPO Model (LoRA Adapters)
- **Location**: `/home/ma/models/Qwen3-0.6B-DPO/`
- **Source**: [AIPlans/Qwen3-0.6B-DPO](https://huggingface.co/AIPlans/Qwen3-0.6B-DPO)
- **Size**: 8.8 MB (LoRA adapters only)
- **Status**: ✅ Downloaded

### 2. Training Scripts Created
- **train_dpo_qwen3.py** - Main DPO training script
- **merge_lora.py** - Merge LoRA with base model
- **merge_dpo_adapters.py** - Merge downloaded DPO adapters
- **quantize_dpo_model.py** - Quantize to GGUF
- **sample_preference_data.jsonl** - Example dataset
- **DPO-Training-README.md** - Documentation
### 3. Colab Notebook Created
- **Qwen3_DPO_Training.ipynb** - Ready for Google Colab
- Free T4 GPU available
- Complete training pipeline
## Quick Start Options
### Option A: Use Existing DPO Model (Fastest)
```bash
# Merge LoRA adapters with base model
python merge_dpo_adapters.py
# Quantize to GGUF
python quantize_dpo_model.py --model_path ./Qwen3-0.6B-DPO-merged --quantization Q4_K_S
```
### Option B: Train Your Own DPO (On GPU)
1. **Upload to Google Colab**:
   - Upload `Qwen3_DPO_Training.ipynb`
   - Upload `train_dpo_qwen3.py`, `merge_lora.py`, `sample_preference_data.jsonl`
   - Set Runtime → GPU (T4)
2. **Run training**:
   ```python
   !python train_dpo_qwen3.py --beta 0.1 --epochs 3
   ```
3. **Download trained model** from Colab
### Option C: Train with Custom Data
1. Create your preference dataset:
   ```json
   {"prompt": "Question?", "chosen": "Good answer", "rejected": "Bad answer"}
   ```
2. Train:
   ```bash
   python train_dpo_qwen3.py --dataset your_data.jsonl --beta 0.1
   ```
## DPO Parameters Guide
| Parameter | Range | Recommendation |
|-----------|-------|----------------|
| **Beta (β)** | 0.05-0.2 | Start with 0.1 |
| **Learning Rate** | 1e-5 to 5e-5 | 2e-5 |
| **Epochs** | 1-5 | 3 |
| **LoRA r** | 8-32 | 16 |
| **LoRA α** | 8-32 | 16 |

## Files Summary

```
/home/ma/models/
├── Qwen3-0.6B-DPO/              # Downloaded DPO adapters (8.8 MB)
├── train_dpo_qwen3.py           # Training script
├── merge_lora.py                # Merge LoRA adapters
├── merge_dpo_adapters.py        # Merge downloaded adapters
├── quantize_dpo_model.py        # Quantize to GGUF
├── sample_preference_data.jsonl # Example dataset
├── DPO-Training-README.md       # Documentation
└── Qwen3_DPO_Training.ipynb     # Colab notebook
```

## Next Steps

1. **For immediate use**: Run `merge_dpo_adapters.py` then `quantize_dpo_model.py`
2. **For custom training**: Use Colab notebook with your data
3. **For production**: Train on GPU with larger dataset (5000+ examples)

## References

- [DPO Paper](https://arxiv.org/abs/2305.18290)
- [AIPlans DPO Model](https://huggingface.co/AIPlans/Qwen3-0.6B-DPO)
- [TRL Documentation](https://huggingface.co/docs/trl)

	# Qwen3-0.6B DPO Training - Complete Setup

	## What's Ready

	### 1. Downloaded DPO Model (LoRA Adapters)
	- Location: `/home/ma/models/Qwen3-0.6B-DPO/`
	- Source: [AIPlans/Qwen3-0.6B-DPO](https://huggingface.co/AIPlans/Qwen3-0.6B-DPO)
	- Size: 8.8 MB (LoRA adapters only)
	- Status: ✅ Downloaded

	### 2. Training Scripts Created
	- train_dpo_qwen3.py - Main DPO training script
	- merge_lora.py - Merge LoRA with base model
	- merge_dpo_adapters.py - Merge downloaded DPO adapters
	- quantize_dpo_model.py - Quantize to GGUF
	- sample_preference_data.jsonl - Example dataset
	- DPO-Training-README.md - Documentation

	### 3. Colab Notebook Created
	- Qwen3_DPO_Training.ipynb - Ready for Google Colab
	- Free T4 GPU available
	- Complete training pipeline

	## Quick Start Options

	### Option A: Use Existing DPO Model (Fastest)

	```bash
	# Merge LoRA adapters with base model
	python merge_dpo_adapters.py

	# Quantize to GGUF
	python quantize_dpo_model.py --model_path ./Qwen3-0.6B-DPO-merged --quantization Q4_K_S
	```

	### Option B: Train Your Own DPO (On GPU)

	1. Upload to Google Colab:
	- Upload `Qwen3_DPO_Training.ipynb`
	- Upload `train_dpo_qwen3.py`, `merge_lora.py`, `sample_preference_data.jsonl`
	- Set Runtime → GPU (T4)

	2. Run training:
	```python
	!python train_dpo_qwen3.py --beta 0.1 --epochs 3
	```

	3. Download trained model from Colab

	### Option C: Train with Custom Data

	1. Create your preference dataset:
	```json
	{"prompt": "Question?", "chosen": "Good answer", "rejected": "Bad answer"}
	```

	2. Train:
	```bash
	python train_dpo_qwen3.py --dataset your_data.jsonl --beta 0.1
	```

	## DPO Parameters Guide

	\| Parameter \| Range \| Recommendation \|
	\|-----------\|-------\|----------------\|
	\| Beta (β) \| 0.05-0.2 \| Start with 0.1 \|
	\| Learning Rate \| 1e-5 to 5e-5 \| 2e-5 \|
	\| Epochs \| 1-5 \| 3 \|
	\| LoRA r \| 8-32 \| 16 \|
	\| LoRA α \| 8-32 \| 16 \|

	## Files Summary

	```
	/home/ma/models/
	├── Qwen3-0.6B-DPO/ # Downloaded DPO adapters (8.8 MB)
	├── train_dpo_qwen3.py # Training script
	├── merge_lora.py # Merge LoRA adapters
	├── merge_dpo_adapters.py # Merge downloaded adapters
	├── quantize_dpo_model.py # Quantize to GGUF
	├── sample_preference_data.jsonl # Example dataset
	├── DPO-Training-README.md # Documentation
	└── Qwen3_DPO_Training.ipynb # Colab notebook
	```

	## Next Steps

	1. For immediate use: Run `merge_dpo_adapters.py` then `quantize_dpo_model.py`
	2. For custom training: Use Colab notebook with your data
	3. For production: Train on GPU with larger dataset (5000+ examples)

	## References

	- [DPO Paper](https://arxiv.org/abs/2305.18290)
	- [AIPlans DPO Model](https://huggingface.co/AIPlans/Qwen3-0.6B-DPO)
	- [TRL Documentation](https://huggingface.co/docs/trl)