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 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
File size: 2,147 Bytes
bfc7d04 | 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 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | // Stack 2.9 Voice Module
//
// Voice integration for the Stack 2.9 AI coding assistant.
// Provides voice input/output capabilities through Python FastAPI backend.
import { VoiceApiClient, initVoiceClient, getVoiceClient } from './VoiceApiClient'
import {
startRecording,
stopRecording,
isRecording,
checkRecordingAvailability,
checkRecordingDependencies,
audioToBase64,
base64ToAudio,
RECORDING_SAMPLE_RATE,
RECORDING_CHANNELS,
type RecordingAvailability,
type RecordingOptions,
} from './VoiceRecording'
import {
VoiceRecordingTool,
VoiceSynthesisTool,
VoiceCloneTool,
VoiceStatusTool,
voiceTools,
type ToolResult,
} from './VoiceTools'
export {
VoiceApiClient,
initVoiceClient,
getVoiceClient,
startRecording,
stopRecording,
isRecording,
checkRecordingAvailability,
checkRecordingDependencies,
audioToBase64,
base64ToAudio,
RECORDING_SAMPLE_RATE,
RECORDING_CHANNELS,
VoiceRecordingTool,
VoiceSynthesisTool,
VoiceCloneTool,
VoiceStatusTool,
voiceTools,
}
export type {
RecordingAvailability,
RecordingOptions,
ToolResult,
}
// Type exports from VoiceApiClient
export interface VoiceConfig {
apiUrl: string
timeout?: number
}
export interface VoiceModel {
name: string
description?: string
}
export interface CloneVoiceRequest {
voiceName: string
audioPath?: string
audioData?: string
}
export interface SynthesizeRequest {
text: string
voiceName: string
language?: string
}
export interface VoiceListResponse {
voices: VoiceModel[]
count: number
}
export interface CloneVoiceResponse {
success: boolean
voiceName: string
message: string
}
// Convenience function to initialize voice with config from environment
export function initVoiceFromEnv(): VoiceApiClient | null {
const apiUrl = process.env.VOICE_API_URL
if (!apiUrl) {
console.warn('[voice] VOICE_API_URL not set, voice features disabled')
return null
}
return initVoiceClient({
apiUrl,
timeout: parseInt(process.env.VOICE_TIMEOUT ?? '30000', 10),
})
}
export default {
initVoiceClient,
getVoiceClient,
initVoiceFromEnv,
voiceTools,
} |