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 /ROADMAP.md

walidsobhie-code

feat: add inference API, quickstart guide, roadmap, and combined tool data

b03a8a0 about 1 month ago

preview code

raw

history blame contribute delete

5.78 kB

	# Stack 2.9 Roadmap

	> Last updated: April 2026

	## Current Status

	### What's Working ✅

	- Basic code generation - The model can generate Python, JavaScript, and other code based on prompts
	- CLI interface - Working command-line interface (`stack.py`, `src/cli/`)
	- Multi-provider support - Ollama, OpenAI, Anthropic, OpenRouter, Together AI integrations
	- 46 built-in tools - File operations, git, shell, web search, memory, task planning
	- Pattern Memory infrastructure - Observer, Learner, Memory components implemented
	- Training pipeline - LoRA fine-tuning scripts, data preparation, model merging
	- Deployment options - Docker, RunPod, Vast.ai, Kubernetes, HuggingFace Spaces
	- 128K context window - Extended from base model's 32K

	### What's Broken or Missing ⚠️

	- Tool calling not trained - Model doesn't reliably use tools; needs fine-tuning on tool patterns
	- Benchmark scores unverifiable - Previous claims removed after audit found only 20/164 HumanEval problems tested
	- Self-evolution not functional - Observer/Learner components exist but not connected to training pipeline
	- Voice integration incomplete - Coqui XTTS integration present but not tested
	- Evaluation infrastructure in progress - New proper evaluation framework built but not run on full benchmarks

	### What Needs Testing 🔧

	- Full HumanEval (164 problems) evaluation
	- Full MBPP (500 problems) evaluation
	- Tool-calling accuracy with real tasks
	- Pattern Memory retrieval and effectiveness
	- Voice input/output pipeline
	- Multi-provider compatibility

	### What Needs Documentation 📚

	- Tool definitions and schemas
	- API reference (internal/ARCHITECTURE.md exists but needs updating)
	- Pattern Memory usage guide
	- Deployment troubleshooting
	- Evaluation methodology

	---

	## Timeline with Milestones

	### Short-Term (1-2 Weeks)

	\| Milestone \| Description \| Status \|
	\|-----------\|-------------\|--------\|
	\| S1.1 \| Run full HumanEval (164 problems) with proper inference \| Not started \|
	\| S1.2 \| Run full MBPP (500 problems) with proper inference \| Not started \|
	\| S1.3 \| Document all 46 tool definitions in `docs/TOOLS.md` \| In progress \|
	\| S1.4 \| Fix evaluation scripts to use real model inference \| Needed \|
	\| S1.5 \| Create minimal reproducible test for tool calling \| Not started \|

	Owner: Community contribution welcome

	### Medium-Term (1-3 Months)

	\| Milestone \| Description \| Status \|
	\|-----------\|-------------\|--------\|
	\| M2.1 \| Fine-tune model on tool-calling patterns (RTMP data) \| Not started \|
	\| M2.2 \| Implement and test self-evolution loop (Observer → Learner → Memory → Trainer) \| Not started \|
	\| M2.3 \| Run full benchmark evaluation and publish verified scores \| Not started \|
	\| M2.4 \| Add MCP server support for external tool integration \| Partial \|
	\| M2.5 \| Voice integration end-to-end testing \| Not started \|
	\| M2.6 \| Implement pattern extraction from production usage \| Not started \|

	Owner: Requires training compute budget or community contribution

	### Long-Term (6+ Months)

	\| Milestone \| Description \| Status \|
	\|-----------\|-------------\|--------\|
	\| L3.1 \| RLHF training for improved tool selection \| Future \|
	\| L3.2 \| Team sync infrastructure (PostgreSQL + FastAPI) \| Designed, not implemented \|
	\| L3.3 \| Federated learning for privacy-preserving updates \| Future \|
	\| L3.4 \| Multi-modal support (images → code) \| Future \|
	\| L3.5 \| Real-time voice-to-voice conversation \| Future \|

	Owner: Long-term vision, needs significant resources

	---

	## How to Contribute

	### By Priority

	1. Run evaluations - Help us verify benchmark scores by running `python stack_2_9_eval/run_proper_evaluation.py`
	2. Test tool calling - Try the model with various tools and report what works/doesn't
	3. Documentation - Improve docs, especially tool definitions and API reference
	4. Bug reports - Open issues with reproduction steps
	5. Code contributions - See CONTRIBUTING.md for guidelines

	### Contribution Areas

	\| Area \| Skill Needed \| Priority \|
	\|------\|--------------\|----------\|
	\| Evaluation \| Python, ML benchmarking \| High \|
	\| Tool calling tests \| Python, CLI usage \| High \|
	\| Documentation \| Technical writing \| Medium \|
	\| Training scripts \| PyTorch, PEFT \| Medium \|
	\| Deployment \| Docker, K8s, Cloud \| Low \|
	\| Pattern Memory \| Vector databases, ML \| Low \|

	### Quick Wins for Contributors

	- Run `python stack.py -c "List files in current directory"` and report if tools work
	- Review `stack/eval/results/` and verify evaluation logs
	- Check `docs/TOOLS.md` accuracy against actual tool implementations
	- Test with different providers (`--provider ollama\|openai\|anthropic`)

	---

	## Technical Notes

	### Known Limitations

	1. Tool calling is not trained - The base model has tool capabilities but Stack 2.9 hasn't been fine-tuned to use them reliably
	2. Pattern Memory is read-only - The system stores patterns but doesn't automatically retrain on them yet
	3. Evaluation uses stub data - Some eval scripts return pre-canned answers instead of running model
	4. Voice integration untested - Code exists but hasn't been validated end-to-end

	### Next Training Run Requirements

	To fix tool calling, the next training run needs:

	- Dataset: `data/rtmp-tools/combined_tools.jsonl` (already generated)
	- Compute: ~1 hour on A100 for LoRA fine-tuning
	- Configuration: Target tool_call logits, use `tool_use_examples.jsonl`

	---

	## Contact

	- Issues: https://github.com/my-ai-stack/stack-2.9/issues
	- Discussions: https://github.com/my-ai-stack/stack-2.9/discussions
	- Discord: (link in README)

	---

	This roadmap is a living document. Updates based on community feedback and project progress.

	# Stack 2.9 Roadmap

	> Last updated: April 2026

	## Current Status

	### What's Working ✅

	- Basic code generation - The model can generate Python, JavaScript, and other code based on prompts
	- CLI interface - Working command-line interface (`stack.py`, `src/cli/`)
	- Multi-provider support - Ollama, OpenAI, Anthropic, OpenRouter, Together AI integrations
	- 46 built-in tools - File operations, git, shell, web search, memory, task planning
	- Pattern Memory infrastructure - Observer, Learner, Memory components implemented
	- Training pipeline - LoRA fine-tuning scripts, data preparation, model merging
	- Deployment options - Docker, RunPod, Vast.ai, Kubernetes, HuggingFace Spaces
	- 128K context window - Extended from base model's 32K

	### What's Broken or Missing ⚠️

	- Tool calling not trained - Model doesn't reliably use tools; needs fine-tuning on tool patterns
	- Benchmark scores unverifiable - Previous claims removed after audit found only 20/164 HumanEval problems tested
	- Self-evolution not functional - Observer/Learner components exist but not connected to training pipeline
	- Voice integration incomplete - Coqui XTTS integration present but not tested
	- Evaluation infrastructure in progress - New proper evaluation framework built but not run on full benchmarks

	### What Needs Testing 🔧

	- Full HumanEval (164 problems) evaluation
	- Full MBPP (500 problems) evaluation
	- Tool-calling accuracy with real tasks
	- Pattern Memory retrieval and effectiveness
	- Voice input/output pipeline
	- Multi-provider compatibility

	### What Needs Documentation 📚

	- Tool definitions and schemas
	- API reference (internal/ARCHITECTURE.md exists but needs updating)
	- Pattern Memory usage guide
	- Deployment troubleshooting
	- Evaluation methodology

	---

	## Timeline with Milestones

	### Short-Term (1-2 Weeks)

	\| Milestone \| Description \| Status \|
	\|-----------\|-------------\|--------\|
	\| S1.1 \| Run full HumanEval (164 problems) with proper inference \| Not started \|
	\| S1.2 \| Run full MBPP (500 problems) with proper inference \| Not started \|
	\| S1.3 \| Document all 46 tool definitions in `docs/TOOLS.md` \| In progress \|
	\| S1.4 \| Fix evaluation scripts to use real model inference \| Needed \|
	\| S1.5 \| Create minimal reproducible test for tool calling \| Not started \|

	Owner: Community contribution welcome

	### Medium-Term (1-3 Months)

	\| Milestone \| Description \| Status \|
	\|-----------\|-------------\|--------\|
	\| M2.1 \| Fine-tune model on tool-calling patterns (RTMP data) \| Not started \|
	\| M2.2 \| Implement and test self-evolution loop (Observer → Learner → Memory → Trainer) \| Not started \|
	\| M2.3 \| Run full benchmark evaluation and publish verified scores \| Not started \|
	\| M2.4 \| Add MCP server support for external tool integration \| Partial \|
	\| M2.5 \| Voice integration end-to-end testing \| Not started \|
	\| M2.6 \| Implement pattern extraction from production usage \| Not started \|

	Owner: Requires training compute budget or community contribution

	### Long-Term (6+ Months)

	\| Milestone \| Description \| Status \|
	\|-----------\|-------------\|--------\|
	\| L3.1 \| RLHF training for improved tool selection \| Future \|
	\| L3.2 \| Team sync infrastructure (PostgreSQL + FastAPI) \| Designed, not implemented \|
	\| L3.3 \| Federated learning for privacy-preserving updates \| Future \|
	\| L3.4 \| Multi-modal support (images → code) \| Future \|
	\| L3.5 \| Real-time voice-to-voice conversation \| Future \|

	Owner: Long-term vision, needs significant resources

	---

	## How to Contribute

	### By Priority

	1. Run evaluations - Help us verify benchmark scores by running `python stack_2_9_eval/run_proper_evaluation.py`
	2. Test tool calling - Try the model with various tools and report what works/doesn't
	3. Documentation - Improve docs, especially tool definitions and API reference
	4. Bug reports - Open issues with reproduction steps
	5. Code contributions - See CONTRIBUTING.md for guidelines

	### Contribution Areas

	\| Area \| Skill Needed \| Priority \|
	\|------\|--------------\|----------\|
	\| Evaluation \| Python, ML benchmarking \| High \|
	\| Tool calling tests \| Python, CLI usage \| High \|
	\| Documentation \| Technical writing \| Medium \|
	\| Training scripts \| PyTorch, PEFT \| Medium \|
	\| Deployment \| Docker, K8s, Cloud \| Low \|
	\| Pattern Memory \| Vector databases, ML \| Low \|

	### Quick Wins for Contributors

	- Run `python stack.py -c "List files in current directory"` and report if tools work
	- Review `stack/eval/results/` and verify evaluation logs
	- Check `docs/TOOLS.md` accuracy against actual tool implementations
	- Test with different providers (`--provider ollama\|openai\|anthropic`)

	---

	## Technical Notes

	### Known Limitations

	1. Tool calling is not trained - The base model has tool capabilities but Stack 2.9 hasn't been fine-tuned to use them reliably
	2. Pattern Memory is read-only - The system stores patterns but doesn't automatically retrain on them yet
	3. Evaluation uses stub data - Some eval scripts return pre-canned answers instead of running model
	4. Voice integration untested - Code exists but hasn't been validated end-to-end

	### Next Training Run Requirements

	To fix tool calling, the next training run needs:

	- Dataset: `data/rtmp-tools/combined_tools.jsonl` (already generated)
	- Compute: ~1 hour on A100 for LoRA fine-tuning
	- Configuration: Target tool_call logits, use `tool_use_examples.jsonl`

	---

	## Contact

	- Issues: https://github.com/my-ai-stack/stack-2.9/issues
	- Discussions: https://github.com/my-ai-stack/stack-2.9/discussions
	- Discord: (link in README)

	---

	This roadmap is a living document. Updates based on community feedback and project progress.