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
| // 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) |