Text Generation
Transformers
English
qwen2
code-generation
python
fine-tuning
Qwen
tools
agent-framework
multi-agent
conversational
Eval Results (legacy)
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
- Transformers
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
| #!/usr/bin/env python3 | |
| """ | |
| MBPP (Mostly Basic Python Programming) benchmark evaluation. | |
| """ | |
| import json | |
| from pathlib import Path | |
| import datetime | |
| def generate_estimate(): | |
| """Estimate based on Qwen2.5-Coder-32B baseline.""" | |
| # Qwen2.5-Coder-32B: ~80% on MBPP (typical for strong coding models) | |
| estimate = { | |
| "model": "Stack 2.9 (estimate)", | |
| "benchmark": "MBPP", | |
| "pass@1": 0.80, | |
| "pass@10": 0.85, | |
| "pass@100": 0.88, | |
| "note": "Estimate based on Qwen2.5-Coder-32B. Actual after training.", | |
| "source": "Qwen2.5-Coder technical report" | |
| } | |
| return estimate | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--estimate-only", action="store_true") | |
| parser.add_argument("--output", type=str, default="stack-2.9-eval/results/mbpp.json") | |
| args = parser.parse_args() | |
| output_path = Path(args.output) | |
| output_path.parent.mkdir(parents=True, exist_ok=True) | |
| print("🔬 MBPP Benchmark Evaluation") | |
| if args.estimate_only: | |
| result = generate_estimate() | |
| else: | |
| # Actual evaluation would go here (similar to HumanEval) | |
| result = { | |
| "note": "Full MBPP evaluation implementation pending", | |
| "status": "framework_ready" | |
| } | |
| with open(output_path, 'w') as f: | |
| json.dump(result, f, indent=2) | |
| print(f"✅ Results saved to {output_path}") | |
| if "pass@1" in result: | |
| print(f" Pass@1 (estimate): {result['pass@1']*100:.1f}%") | |
| if __name__ == "__main__": | |
| import argparse, datetime | |
| main() |