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
| import time | |
| import os | |
| from voice_client import VoiceClient | |
| from stack_voice_integration import StackWithVoice | |
| # Configuration | |
| STACK_API_URL = "http://localhost:5000" | |
| VOICE_API_URL = "http://localhost:8000" | |
| DEFAULT_VOICE = "default" | |
| # Initialize clients | |
| voice_client = VoiceClient(VOICE_API_URL) | |
| stack_voice = StackWithVoice(STACK_API_URL, VOICE_API_URL) | |
| # Helper function to play audio (placeholder) | |
| def play_audio(audio_data: bytes) -> None: | |
| """Play audio data (placeholder implementation)""" | |
| output_path = "./output.wav" | |
| voice_client.download_audio(audio_data, output_path) | |
| print(f"Audio saved to {output_path}") | |
| print("To play audio, use: open output.wav (macOS) or your preferred audio player") | |
| # Example 1: Basic voice chat | |
| print("\n=== Example 1: Basic Voice Chat ===") | |
| print("This example simulates a voice conversation with the coding assistant.") | |
| print("In a real implementation, you would provide actual audio files.") | |
| # Create a test prompt audio file (placeholder) | |
| test_prompt = "How do I create a REST API in Python using FastAPI?" | |
| with open("test_prompt.txt", 'w') as f: | |
| f.write(test_prompt) | |
| print(f"\nTest prompt: {test_prompt}") | |
| # Simulate voice chat | |
| print("\nSimulating voice chat...") | |
| response_audio = stack_voice.voice_chat("test_prompt.wav", DEFAULT_VOICE) | |
| if response_audio: | |
| play_audio(response_audio) | |
| print("\nVoice chat completed successfully!") | |
| else: | |
| print("\nVoice chat failed or no response received") | |
| # Example 2: Voice command to code generation | |
| print("\n\n=== Example 2: Voice Command to Code Generation ===") | |
| print("This example shows how to use voice commands to generate code.") | |
| code_command = "Create a Python class for a banking system with account management" | |
| print(f"\nVoice command: {code_command}") | |
| # Simulate voice command | |
| print("\nExecuting voice command...") | |
| command_response = stack_voice.voice_command(code_command, DEFAULT_VOICE) | |
| if command_response: | |
| play_audio(command_response) | |
| print("\nVoice command executed successfully!") | |
| else: | |
| print("\nVoice command failed or no response received") | |
| # Example 3: Streaming voice responses | |
| print("\n\n=== Example 3: Streaming Voice Responses ===") | |
| print("This example demonstrates streaming voice responses.") | |
| streaming_prompt = "Explain how to implement machine learning in Python" | |
| print(f"\nStreaming prompt: {streaming_prompt}") | |
| # Simulate streaming voice chat | |
| print("\nStarting streaming voice chat...") | |
| stack_voice.streaming_voice_chat("test_prompt.wav", DEFAULT_VOICE) | |
| print("\nStreaming voice chat completed!") | |
| # Example 4: Error handling | |
| print("\n\n=== Example 4: Error Handling ===") | |
| print("This example demonstrates error handling in the voice integration.") | |
| # Test with invalid voice name | |
| print("\nTesting with invalid voice name...") | |
| try: | |
| invalid_response = stack_voice.voice_chat("test_prompt.wav", "nonexistent_voice") | |
| if invalid_response: | |
| play_audio(invalid_response) | |
| except Exception as e: | |
| print(f"Error handled correctly: {e}") | |
| # Test with empty prompt | |
| print("\nTesting with empty prompt...") | |
| try: | |
| empty_response = stack_voice.voice_chat("empty_prompt.wav", DEFAULT_VOICE) | |
| if empty_response: | |
| play_audio(empty_response) | |
| except Exception as e: | |
| print(f"Error handled correctly: {e}") | |
| # Example 5: Voice model management | |
| print("\n\n=== Example 5: Voice Model Management ===") | |
| print("This example shows how to manage voice models.") | |
| print("\nListing available voices...") | |
| available_voices = voice_client.list_voices() | |
| print(f"Available voices: {available_voices}") | |
| # Note: Voice cloning requires actual audio files | |
| # print("\nCloning a new voice...") | |
| # clone_result = voice_client.clone_voice("my_audio_sample.wav", "custom_voice") | |
| # print(f"Clone result: {clone_result}") | |
| print("\nAll examples completed!") | |
| print("\n=== Next Steps ===") | |
| print("1. Implement actual speech-to-text for audio_to_text()") | |
| print("2. Integrate with real Stack 2.9 API") | |
| print("3. Add proper audio playback functionality") | |
| print("4. Implement streaming TTS properly") | |
| print("5. Add voice model training with Coqui TTS") |