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: 4,089 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | // Voice API Client - Connects to Python voice server (Coqui TTS)
//
// This client provides TypeScript bindings to the Python FastAPI voice service
// for voice cloning and text-to-speech synthesis.
export interface VoiceConfig {
apiUrl: string
timeout?: number
}
export interface VoiceModel {
name: string
description?: string
}
export interface CloneVoiceRequest {
voiceName: string
audioPath?: string
audioData?: string // base64 encoded audio
}
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
}
export class VoiceApiClient {
private apiUrl: string
private timeout: number
constructor(config: VoiceConfig) {
this.apiUrl = config.apiUrl.replace(/\/$/, '')
this.timeout = config.timeout ?? 30000
}
/**
* List all available voice models
*/
async listVoices(): Promise<VoiceListResponse> {
const response = await fetch(`${this.apiUrl}/voices`, {
method: 'GET',
headers: { 'Content-Type': 'application/json' },
signal: AbortSignal.timeout(this.timeout),
})
if (!response.ok) {
throw new Error(`Failed to list voices: ${response.status} ${response.statusText}`)
}
return response.json() as Promise<VoiceListResponse>
}
/**
* Clone a voice from audio sample(s)
*/
async cloneVoice(request: CloneVoiceRequest): Promise<CloneVoiceResponse> {
const response = await fetch(`${this.apiUrl}/clone`, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(request),
signal: AbortSignal.timeout(this.timeout),
})
if (!response.ok) {
throw new Error(`Failed to clone voice: ${response.status} ${response.statusText}`)
}
return response.json() as Promise<CloneVoiceResponse>
}
/**
* Synthesize speech with a cloned voice
* Returns audio data as a Blob
*/
async synthesize(request: SynthesizeRequest): Promise<Blob> {
const response = await fetch(`${this.apiUrl}/synthesize`, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(request),
signal: AbortSignal.timeout(this.timeout),
})
if (!response.ok) {
throw new Error(`Failed to synthesize: ${response.status} ${response.statusText}`)
}
return response.blob()
}
/**
* Stream speech synthesis for real-time applications
*/
async *streamSynthesize(request: SynthesizeRequest): AsyncGenerator<Uint8Array> {
const response = await fetch(`${this.apiUrl}/synthesize_stream`, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(request),
signal: AbortSignal.timeout(this.timeout),
})
if (!response.ok) {
throw new Error(`Failed to stream synthesize: ${response.status} ${response.statusText}`)
}
if (!response.body) {
throw new Error('Empty response body')
}
const reader = response.body.getReader()
const decoder = new TextDecoder()
try {
while (true) {
const { done, value } = await reader.read()
if (done) break
yield value
}
} finally {
reader.releaseLock()
}
}
/**
* Check if voice server is available
*/
async healthCheck(): Promise<boolean> {
try {
const response = await fetch(`${this.apiUrl}/health`, {
method: 'GET',
signal: AbortSignal.timeout(5000),
})
return response.ok
} catch {
return false
}
}
}
// Default client instance
let defaultClient: VoiceApiClient | null = null
/**
* Initialize the default voice client
*/
export function initVoiceClient(config: VoiceConfig): VoiceApiClient {
defaultClient = new VoiceApiClient(config)
return defaultClient
}
/**
* Get the default voice client
*/
export function getVoiceClient(): VoiceApiClient | null {
return defaultClient
} |