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: 3,245 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 | // Extract tool schemas from RTMP for training data
//
// This script extracts tool definitions from the RTMP codebase
// and adds them to stack-2.9's training data catalog.
import { readdir, readFile, writeFile } from 'fs/promises'
import { join, basename } from 'path'
const RTMP_TOOLS_DIR = '/Users/walidsobhi/.openclaw/workspace/RTMP/tools'
const STACK_CATALOG = '/Users/walidsobhi/.openclaw/workspace/stack-2.9/training-data/tools/catalog.json'
interface ToolSchema {
tool: string
description: string
hasPrompt: boolean
hasImplementation: boolean
inputSchema: Record<string, unknown>
}
async function extractToolSchemas(): Promise<ToolSchema[]> {
const tools: ToolSchema[] = []
const toolDirs = await readdir(RTMP_TOOLS_DIR)
for (const toolDir of toolDirs) {
const toolPath = join(RTMP_TOOLS_DIR, toolDir)
const stat = await readdir(toolPath).then(() => true).catch(() => false)
if (!stat) continue
// Try to extract tool name and description from tool files
let description = ''
let hasPrompt = false
let hasImplementation = false
try {
// Check for prompt.ts
const promptPath = join(toolPath, 'prompt.ts')
const promptContent = await readFile(promptPath, 'utf-8')
hasPrompt = true
// Extract first meaningful comment as description
const comments = promptContent.match(/\/\*\*[\s\S]*?\*\//g)
if (comments && comments.length > 0) {
const comment = comments[0]
description = comment
.replace(/\/\*\*|\*\//g, '')
.replace(/^\s*\*\s?/gm, '')
.trim()
.slice(0, 200)
}
} catch {
// No prompt.ts
}
try {
// Check for implementation files
const toolFiles = await readdir(toolPath)
hasImplementation = toolFiles.some(f =>
f.endsWith('.ts') || f.endsWith('.tsx')
)
} catch {
// Ignore
}
// Format tool name (remove Tool suffix for cleaner names)
const toolName = toolDir.replace(/Tool$/, '')
tools.push({
tool: toolDir,
description: description || `${toolName} tool`,
hasPrompt,
hasImplementation,
inputSchema: {}
})
}
return tools
}
async function main() {
console.log('Extracting tool schemas from RTMP...')
const tools = await extractToolSchemas()
console.log(`Found ${tools.length} tools`)
// Read existing catalog
let existingTools: ToolSchema[] = []
try {
const existingContent = await readFile(STACK_CATALOG, 'utf-8')
existingTools = JSON.parse(existingContent)
} catch {
console.log('No existing catalog found')
}
// Merge with existing (avoid duplicates)
const existingNames = new Set(existingTools.map(t => t.tool))
const newTools = tools.filter(t => !existingNames.has(t.tool))
console.log(`Adding ${newTools.length} new tools`)
// Combine
const allTools = [...existingTools, ...newTools]
// Write updated catalog
await writeFile(STACK_CATALOG, JSON.stringify(allTools, null, 2))
console.log(`Updated catalog with ${allTools.length} tools`)
// Also print summary
console.log('\nNew tools added:')
for (const tool of newTools) {
console.log(` - ${tool.tool}`)
}
}
main().catch(console.error) |