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
File size: 1,700 Bytes
b6ae7b8 f80360c b6ae7b8 f80360c b6ae7b8 f80360c 0908455 f80360c b6ae7b8 f80360c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | #!/usr/bin/env python3
"""
Stack 2.9 - Main Entry Point
Launch the Stack 2.9 CLI and agent interface.
"""
import sys
import argparse
from pathlib import Path
# Add directories to path
stack_cli_dir = Path(__file__).parent / "stack_cli"
stack_2_9_cli = Path(__file__).parent / "stack-2-9-cli"
stack_2_9_eval = Path(__file__).parent / "stack-2-9-eval"
stack_2_9_training = Path(__file__).parent / "stack-2-9-training"
paths = [str(stack_cli_dir), str(stack_2_9_cli), str(stack_2_9_eval), str(stack_2_9_training)]
for p in paths:
if Path(p).exists():
sys.path.insert(0, p)
def main():
parser = argparse.ArgumentParser(description="Stack 2.9 CLI")
parser.add_argument("--provider", "-p", choices=["ollama", "openai", "anthropic", "together"],
default="ollama", help="Model provider")
parser.add_argument("--model", "-m", type=str, help="Model name")
parser.add_argument("--chat", "-c", action="store_true", help="Start in chat mode")
parser.add_argument("--eval", "-e", choices=["mbpp", "human_eval", "gsm8k", "all"],
help="Run evaluation benchmark")
parser.add_argument("--patterns", action="store_true", help="View pattern statistics")
args = parser.parse_args()
# Try new CLI first, fall back to old
try:
from stack_2_9_cli.main import main as new_main
new_main()
except ImportError:
try:
from stack_cli.cli import main as cli_main
cli_main()
except ImportError as e:
print(f"Error: {e}")
print("Install dependencies: pip install -r requirements.txt")
sys.exit(1)
if __name__ == "__main__":
main() |