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: 6,944 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 | // Voice Tools - Tools for voice input/output in the AI assistant
//
// Provides tools for:
// - VoiceRecordingTool: Record voice commands
// - VoiceSynthesisTool: Speak responses
// - VoiceCloneTool: Clone voices from samples
import { log } from '../utils/logger'
import { initVoiceClient, getVoiceClient } from './VoiceApiClient'
import {
startRecording,
stopRecording,
isRecording,
checkRecordingAvailability,
audioToBase64,
type RecordingAvailability
} from './VoiceRecording'
// Tool result types
export interface ToolResult {
success: boolean
data?: unknown
error?: string
}
// Voice config type
export interface VoiceConfig {
apiUrl: string
timeout?: number
}
// βββ Voice Recording Tool βββ
/**
* VoiceRecordingTool - Records voice input from microphone
*/
export class VoiceRecordingTool {
name = 'VoiceRecordingTool'
description = 'Record voice input from the microphone for voice commands'
async execute(options?: { maxDuration?: number }): Promise<ToolResult> {
try {
// Check availability
const availability = await checkRecordingAvailability()
if (!availability.available) {
return { success: false, error: availability.reason ?? 'Recording not available' }
}
// Start recording
let audioChunks: Buffer[] = []
const started = await startRecording(
(chunk) => {
audioChunks.push(chunk)
},
() => {
log('[voice] Recording ended')
},
{ silenceDetection: true }
)
if (!started) {
return { success: false, error: 'Failed to start recording' }
}
// Wait for recording to end (silence detection)
await new Promise<void>((resolve) => {
const checkInterval = setInterval(() => {
if (!isRecording()) {
clearInterval(checkInterval)
resolve()
}
}, 100)
// Timeout after maxDuration
if (options?.maxDuration) {
setTimeout(() => {
clearInterval(checkInterval)
stopRecording()
resolve()
}, options.maxDuration)
}
})
// Combine audio chunks
const audioBuffer = Buffer.concat(audioChunks)
const base64Audio = audioToBase64(audioBuffer)
return {
success: true,
data: {
audio: base64Audio,
duration: audioBuffer.length / (16000 * 2),
sampleRate: 16000,
channels: 1,
},
}
} catch (error) {
log('[voice] Recording error', error)
return { success: false, error: String(error) }
}
}
stop(): void {
stopRecording()
}
}
// βββ Voice Synthesis Tool βββ
/**
* VoiceSynthesisTool - Convert text to speech using cloned voice
*/
export class VoiceSynthesisTool {
private client: ReturnType<typeof getVoiceClient>
constructor(config?: VoiceConfig) {
if (config) {
this.client = initVoiceClient(config)
} else {
this.client = getVoiceClient()
}
}
name = 'VoiceSynthesisTool'
description = 'Convert text to speech using a cloned voice'
async execute(request: { text: string; voiceName?: string }): Promise<ToolResult> {
const client = this.client
if (!client) {
return {
success: false,
error: 'Voice client not initialized. Call initVoiceClient() first.',
}
}
try {
const audioBlob = await client.synthesize({
text: request.text,
voiceName: request.voiceName ?? 'default',
})
// Convert blob to base64
const arrayBuffer = await audioBlob.arrayBuffer()
const base64Audio = btoa(
new Uint8Array(arrayBuffer).reduce((data, byte) => data + String.fromCharCode(byte), '')
)
return {
success: true,
data: {
audio: base64Audio,
format: 'wav',
text: request.text,
},
}
} catch (error) {
log('[voice] Synthesis error', error)
return { success: false, error: String(error) }
}
}
async *streamExecute(request: { text: string; voiceName?: string }): AsyncGenerator<Uint8Array> {
const client = this.client
if (!client) {
throw new Error('Voice client not initialized')
}
yield* client.streamSynthesize({
text: request.text,
voiceName: request.voiceName ?? 'default',
})
}
}
// βββ Voice Clone Tool βββ
/**
* VoiceCloneTool - Clone a voice from audio samples
*/
export class VoiceCloneTool {
private client: ReturnType<typeof getVoiceClient>
constructor(config?: VoiceConfig) {
if (config) {
this.client = initVoiceClient(config)
} else {
this.client = getVoiceClient()
}
}
name = 'VoiceCloneTool'
description = 'Clone a voice from audio samples for use in synthesis'
async execute(request: { voiceName: string; audioPath?: string; audioData?: string }): Promise<ToolResult> {
const client = this.client
if (!client) {
return {
success: false,
error: 'Voice client not initialized. Call initVoiceClient() first.',
}
}
try {
const result = await client.cloneVoice({
voiceName: request.voiceName,
audioPath: request.audioPath,
audioData: request.audioData,
})
return {
success: result.success,
data: result,
}
} catch (error) {
log('[voice] Clone error', error)
return { success: false, error: String(error) }
}
}
}
// βββ Voice Status Tool βββ
/**
* VoiceStatusTool - Check voice service availability
*/
export class VoiceStatusTool {
private client: ReturnType<typeof getVoiceClient>
constructor(config?: VoiceConfig) {
if (config) {
this.client = initVoiceClient(config)
} else {
this.client = getVoiceClient()
}
}
name = 'VoiceStatusTool'
description = 'Check voice service status and list available voices'
async execute(): Promise<ToolResult> {
try {
// Check recording availability
const recordingAvail = await checkRecordingAvailability()
// Check voice API availability
let apiAvailable = false
let voices: string[] = []
const client = this.client
if (client) {
apiAvailable = await client.healthCheck()
if (apiAvailable) {
const voiceList = await client.listVoices()
voices = voiceList.voices.map((v: { name: string }) => v.name)
}
}
return {
success: true,
data: {
recording: recordingAvail,
api: apiAvailable,
voices,
},
}
} catch (error) {
return { success: false, error: String(error) }
}
}
}
// βββ Tool Registry βββ
export const voiceTools = {
VoiceRecordingTool,
VoiceSynthesisTool,
VoiceCloneTool,
VoiceStatusTool,
}
export default voiceTools |