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 / docs /LAUNCH_PLAN.md

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Stack 2.9 Official Launch Plan

This document outlines the steps to officially release Stack 2.9.

Phase 1: Testing & Validation (Immediate)

1.1 Unit Tests

# Run existing tests
cd /Users/walidsobhi/.openclaw/workspace/stack-2.9
python -m pytest samples/ -v

# Expected: All tests pass

1.2 Integration Tests

# Test CLI functionality
python -m pytest samples/integration/ -v

# Test tools
python -m pytest samples/unit/test_tools.py -v

1.3 Model Benchmark

# Download benchmark datasets
python scripts/download_benchmark_datasets.py --data-dir ./data

# Run HumanEval (164 problems)
python stack/eval/run_proper_evaluation.py \
    --benchmark humaneval \
    --provider ollama \
    --model qwen2.5-coder:7b \
    --k-samples 10 \
    --output-dir ./results

# Run MBPP (500 problems)
python stack/eval/run_proper_evaluation.py \
    --benchmark mbpp \
    --provider ollama \
    --model qwen2.5-coder:7b \
    --k-samples 10 \
    --output-dir ./results

1.4 Quick Smoke Test

# Test basic functionality
python stack/eval/simple_test.py

Phase 2: Demo & Showcase (Day 1-2)

2.1 Create Working Demo

# Create a simple Gradio demo
cd stack/deploy
python app.py  # Should start web interface

2.2 Record Demo Video

Show voice input/output
Show code generation
Show tool usage

2.3 Create Screenshots

CLI interface
Web UI
API responses

Phase 3: Documentation Finalization (Day 2-3)

3.1 Verify All Docs Present

README.md              ✅ Main documentation
stack/deploy/FREE_DEPLOYMENT.md  ✅ Free deployment guide
stack/deploy/README.md ✅ Deployment docs
DIRECTORY_STRUCTURE.md ✅ Project structure

3.2 Update Version

# Update version in files
- README.md
- pyproject.toml
- package.json

Phase 4: Deployment Setup (Day 3-4)

4.1 HuggingFace Space

Create account at huggingface.co
New Space → Docker → Python 3.11
Push stack/deploy/hfSpaces/*
Get public URL

4.2 Model Upload

# Upload fine-tuned model
python stack/training/upload_hf.py \
    --model-path ./output/stack-2.9-7b \
    --repo-id yourusername/stack-2.9-7b

4.3 Test Free Deployment

# Test on free tier
cd stack/deploy/hfSpaces
docker build -t stack-2.9 .
docker run -p 7860:7860 stack-2.9

Phase 5: Launch & Promote (Day 5-7)

5.1 Social Media

Twitter/X thread
LinkedIn post
Hacker News submission
Reddit r/LocalLLaMA

5.2 Platforms

Submit to OpenRouter
Submit to HuggingFace
Add to awesome-llm list

5.3 Community

Discord server invite
GitHub discussions

Launch Checklist

Task	Status	Notes
Unit tests pass	⬜	Run `pytest samples/`
Integration tests pass	⬜	Run `pytest samples/integration/`
Benchmarks run	⬜	HumanEval + MBPP
Demo works	⬜	Gradio UI test
Free deployment works	⬜	HF Spaces test
Documentation complete	⬜	All docs in place
Version updated	⬜	Set to 1.0.0
HF Space deployed	⬜	Get public URL
Model uploaded	⬜	To HuggingFace
Social media ready	⬜	Posts prepared

Quick Test Commands

# 1. Test imports
cd /Users/walidsobhi/.openclaw/workspace/stack-2.9
python -c "from stack.eval.model_client import create_model_client; print('OK')"

# 2. Test CLI
python -m stack.cli.cli --help

# 3. Test eval
python stack/eval/simple_test.py

# 4. Run benchmarks
python stack/eval/run_proper_evaluation.py --benchmark humaneval --provider ollama --model qwen2.5-coder:7b --k-samples 5

# 5. Start web UI
cd stack/deploy && python app.py

Expected Outcomes

After launch:

✅ Working open-source AI coding assistant
✅ Free deployment on HF Spaces
✅ Fine-tunable on Together AI
✅ 46 tool schemas trained
✅ OpenAI-compatible API

Contact & Support

Issues: https://github.com/my-ai-stack/stack-2.9/issues
Discussions: https://github.com/my-ai-stack/stack-2.9/discussions