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
| # Stack 2.9 Quick HumanEval Evaluation Wrapper | |
| # Usage: ./quick_human_eval.sh [provider] [model] [num_samples] | |
| # Example: ./quick_human_eval.sh ollama qwen2.5-coder:32b 20 | |
| set -e | |
| SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" | |
| cd "$SCRIPT_DIR" | |
| # Defaults | |
| PROVIDER="${1:-ollama}" | |
| MODEL="${2:-qwen2.5-coder:32b}" | |
| MAX_PROBLEMS="${3:-20}" | |
| echo "========================================" | |
| echo "Stack 2.9 HumanEval Quick Evaluation" | |
| echo "========================================" | |
| echo "Provider: $PROVIDER" | |
| echo "Model: $MODEL" | |
| echo "Problems: $MAX_PROBLEMS" | |
| echo "" | |
| # Check if vllm is available | |
| if command -v vllm &> /dev/null; then | |
| USE_VLLM="--use-vllm" | |
| echo "β vLLM detected - will use for faster inference" | |
| else | |
| USE_VLLM="" | |
| echo "β vLLM not found - using standard inference" | |
| fi | |
| # Check provider availability | |
| case "$PROVIDER" in | |
| ollama) | |
| if command -v ollama &> /dev/null; then | |
| echo "β Ollama available" | |
| # Check if model is loaded | |
| if curl -s http://localhost:11434/api/tags &> /dev/null; then | |
| echo "β Ollama server running" | |
| else | |
| echo "β Ollama server not running - start with: ollama serve" | |
| fi | |
| else | |
| echo "β Ollama not installed - will attempt anyway" | |
| fi | |
| ;; | |
| openai) | |
| if [ -z "$OPENAI_API_KEY" ]; then | |
| echo "β OPENAI_API_KEY not set" | |
| else | |
| echo "β OpenAI API key configured" | |
| fi | |
| ;; | |
| anthropic) | |
| if [ -z "$ANTHROPIC_API_KEY" ]; then | |
| echo "β ANTHROPIC_API_KEY not set" | |
| else | |
| echo "β Anthropic API key configured" | |
| fi | |
| ;; | |
| esac | |
| echo "" | |
| echo "Running evaluation..." | |
| echo "----------------------------------------" | |
| # Run the evaluation | |
| python3 -m benchmarks.human_eval \ | |
| --provider "$PROVIDER" \ | |
| --model "$MODEL" \ | |
| --max-problems "$MAX_PROBLEMS" \ | |
| --timeout 30 \ | |
| $USE_VLLM | |
| echo "" | |
| echo "========================================" | |
| echo "Evaluation complete!" | |
| echo "========================================" | |
| echo "" | |
| echo "Results saved to: results/humaneval.json" | |
| echo "" | |
| echo "To run full 164-problem benchmark:" | |
| echo " 1. Download full HumanEval dataset" | |
| echo " 2. Use GPU with 80GB VRAM (A100/H100)" | |
| echo " 3. See HUMAN_EVAL_PLAN.md for details" |