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", device_map="auto") 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 Settings
- 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
| // Voice Integration Example - Demonstrates voice tools with Stack 2.9 | |
| // | |
| // This example shows how to: | |
| // 1. Initialize the voice client | |
| // 2. Clone a voice from audio sample | |
| // 3. Record voice commands | |
| // 4. Synthesize speech responses | |
| import { | |
| initVoiceClient, | |
| VoiceRecordingTool, | |
| VoiceSynthesisTool, | |
| VoiceCloneTool, | |
| VoiceStatusTool, | |
| } from '../voice/index.js' | |
| import { log } from '../utils/logger.js' | |
| /** | |
| * Example: Initialize voice client and check status | |
| */ | |
| async function checkVoiceStatus() { | |
| log('Checking voice service status...') | |
| // Initialize client (or use environment variables) | |
| const client = initVoiceClient({ | |
| apiUrl: process.env.VOICE_API_URL ?? 'http://localhost:8000', | |
| }) | |
| const statusTool = new VoiceStatusTool() | |
| const result = await statusTool.execute() | |
| log('Voice status:', result) | |
| return result | |
| } | |
| /** | |
| * Example: Clone a voice from audio sample | |
| */ | |
| async function cloneVoiceExample() { | |
| log('Cloning voice from sample...') | |
| const client = initVoiceClient({ | |
| apiUrl: process.env.VOICE_API_URL ?? 'http://localhost:8000', | |
| }) | |
| const cloneTool = new VoiceCloneTool() | |
| const result = await cloneTool.execute({ | |
| voiceName: 'my_voice', | |
| audioPath: './audio_samples/my_voice.wav', | |
| }) | |
| log('Clone result:', result) | |
| return result | |
| } | |
| /** | |
| * Example: Record voice command | |
| */ | |
| async function recordVoiceCommand() { | |
| log('Starting voice recording...') | |
| const recordingTool = new VoiceRecordingTool() | |
| // Record with max 30 second duration | |
| const result = await recordingTool.execute({ maxDuration: 30000 }) | |
| if (result.success) { | |
| const data = result.data as { duration?: number; sampleRate?: number } | undefined | |
| log('Recording captured:', { | |
| duration: data?.duration, | |
| sampleRate: data?.sampleRate, | |
| }) | |
| } else { | |
| log('Recording failed:', result.error) | |
| } | |
| return result | |
| } | |
| /** | |
| * Example: Synthesize speech response | |
| */ | |
| async function synthesizeResponse(text: string) { | |
| log(`Synthesizing: "${text}"`) | |
| const client = initVoiceClient({ | |
| apiUrl: process.env.VOICE_API_URL ?? 'http://localhost:8000', | |
| }) | |
| const synthTool = new VoiceSynthesisTool() | |
| const result = await synthTool.execute({ | |
| text, | |
| voiceName: 'my_voice', | |
| }) | |
| if (result.success) { | |
| log('Audio generated successfully') | |
| } else { | |
| log('Synthesis failed:', result.error) | |
| } | |
| return result | |
| } | |
| /** | |
| * Example: Complete voice conversation workflow | |
| */ | |
| async function voiceConversation() { | |
| // 1. Check status | |
| await checkVoiceStatus() | |
| // 2. Record user's voice command | |
| const recording = await recordVoiceCommand() | |
| if (!recording.success) { | |
| log('Cannot proceed without voice input') | |
| return | |
| } | |
| // 3. In real implementation, send audio to STT service | |
| // const text = await transcribe(recording.data.audio) | |
| // 4. Process with Stack 2.9 (simulated) | |
| const responseText = 'I have analyzed your code and found 3 potential improvements.' | |
| // 5. Synthesize response | |
| await synthesizeResponse(responseText) | |
| } | |
| // Run examples if this is the main module | |
| if (import.meta.url === `file://${process.argv[1]}`) { | |
| log('Running voice integration examples...') | |
| // Check status | |
| await checkVoiceStatus() | |
| // Uncomment to run other examples: | |
| // await cloneVoiceExample() | |
| // await recordVoiceCommand() | |
| // await synthesizeResponse('Hello, this is a test response.') | |
| // await voiceConversation() | |
| } | |
| export default { | |
| checkVoiceStatus, | |
| cloneVoiceExample, | |
| recordVoiceCommand, | |
| synthesizeResponse, | |
| voiceConversation, | |
| } |