Instructions to use my-ai-stack/Stack-2-9-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use my-ai-stack/Stack-2-9-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="my-ai-stack/Stack-2-9-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("my-ai-stack/Stack-2-9-finetuned")
model = AutoModelForCausalLM.from_pretrained("my-ai-stack/Stack-2-9-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use my-ai-stack/Stack-2-9-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "my-ai-stack/Stack-2-9-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "my-ai-stack/Stack-2-9-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/my-ai-stack/Stack-2-9-finetuned

SGLang

How to use my-ai-stack/Stack-2-9-finetuned 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 "my-ai-stack/Stack-2-9-finetuned" \
    --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": "my-ai-stack/Stack-2-9-finetuned",
		"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 "my-ai-stack/Stack-2-9-finetuned" \
        --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": "my-ai-stack/Stack-2-9-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use my-ai-stack/Stack-2-9-finetuned with Docker Model Runner:
```
docker model run hf.co/my-ai-stack/Stack-2-9-finetuned
```

Stack-2-9-finetuned

File size: 3,772 Bytes

fcb2b04
 
 
 
 
 
cc5f046
fcb2b04
 
 
 
 
 
 
 
 
 
 
 
cc5f046
fcb2b04

# OpenRouter Submission - Stack 2.9

## Model Information

**Model Name**: Qwen/Qwen2.5-Coder-32B
**Fine-Tuned Version**: Stack 2.9 (OpenClaw tool patterns)
**Context Length**: 131072 tokens
**Architecture**: Transformer-based
**Parameters**: 32 billion

## Capabilities

### Core Capabilities
- **Code Generation**: Multi-language code writing and completion
- **Tool Use**: Native integration with OpenClaw tool patterns
- **Voice Integration Ready**: Compatible with voice cloning systems
- **API Compatibility**: OpenAI-compatible endpoints

### Advanced Features
- **Context Understanding**: 128K token context window
- **Multi-file Operations**: Work across entire codebases
- **Error Detection**: Identify and suggest fixes
- **Code Review**: Automated quality analysis
- **Documentation Generation**: Auto-create API docs

## Pricing Proposal

### Free Tier
- **Requests**: 100,000 tokens/day
- **Concurrent Requests**: 5
- **Features**: All core capabilities

### Pay-Per-Use
- **Tier 1**: $0.50 per 1M tokens
- **Tier 2**: $0.40 per 1M tokens (for volumes > 100M tokens)
- **Tier 3**: $0.30 per 1M tokens (for volumes > 500M tokens)

### Enterprise
- **Custom Pricing**: Contact for volume discounts
- **SLA**: 99.9% uptime guarantee
- **Support**: Priority support included

## Review Process Timeline

### Submission Phase (Week 1)
- Initial submission and documentation review
- Model capabilities verification
- API endpoint testing

### Testing Phase (Weeks 2-3)
- Performance benchmarking
- Safety and bias evaluation
- Integration testing

### Approval Phase (Week 4)
- Final review and approval
- Listing preparation
- Launch planning

## Contact Information

**Primary Contact**: Stack 2.9 Team
**Email**: stack29@openclaw.org
**Website**: https://stack2.9.openclaw.org
**GitHub**: https://github.com/my-ai-stack/stack-2.9

## Unique Value Proposition

### Why Stack 2.9?

1. **Voice-Enabled Coding**: The only open-source coding assistant with native voice integration
2. **Tool Pattern Excellence**: Fine-tuned on OpenClaw's extensive tool-use patterns
3. **Cost-Effective**: Significantly cheaper than commercial alternatives
4. **Self-Hosting Freedom**: Apache 2.0 license allows unrestricted deployment
5. **Community-Driven**: Developed by the open-source community

### Competitive Advantages

- **Voice Integration**: Unlike Claude Code or GitHub Copilot, Stack 2.9 supports voice commands
- **Open Source**: Fully transparent with Apache 2.0 licensing
- **Tool Patterns**: Specialized in OpenClaw tool patterns for superior tool use
- **Cost**: Free tier available, pay-per-use model
- **Flexibility**: Self-hosting option for complete control

### Target Markets

- **Individual Developers**: Free tier for hobbyists and students
- **Startups**: Cost-effective alternative to commercial solutions
- **Enterprises**: Self-hosting option for data privacy
- **Educational Institutions**: Open source for learning and research

## Safety and Ethics

### Safety Measures
- **Bias Mitigation**: Fine-tuning includes bias reduction techniques
- **Content Filtering**: Built-in content safety filters
- **Tool Validation**: All tool calls are validated before execution

### Ethical Considerations
- **Open Source**: Transparent development process
- **Community Governance**: Community-driven development
- **Responsible AI**: Committed to ethical AI development

## Performance Metrics

### Benchmark Results
- **HumanEval**: 75% pass@1 (estimated)
- **MBPP**: 80% pass@1 (estimated)
- **Tokens/Second**: 25-30 tokens/second on A100 GPU

### Latency
- **Average Response Time**: 2-3 seconds
- **Streaming**: Real-time response generation

---

**Stack 2.9** - Revolutionizing coding with voice and open source. Ready for OpenRouter listing approval.