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 / stack /internal /OPENROUTER_SUBMISSION.md

walidsobhie-code

Update documentation to 128K context

cc5f046 about 1 month ago

preview code

raw

history blame contribute delete

3.77 kB

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?

Voice-Enabled Coding: The only open-source coding assistant with native voice integration
Tool Pattern Excellence: Fine-tuned on OpenClaw's extensive tool-use patterns
Cost-Effective: Significantly cheaper than commercial alternatives
Self-Hosting Freedom: Apache 2.0 license allows unrestricted deployment
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.