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
| #!/usr/bin/env python3 | |
| """ | |
| Extract training data from RTMP tools for Stack 2.9 | |
| Creates synthetic tool-use examples from the RTMP codebase | |
| """ | |
| import os | |
| import json | |
| from pathlib import Path | |
| RTMP_DIR = "/Users/walidsobhi/.openclaw/workspace/RTMP" | |
| OUTPUT_DIR = "/Users/walidsobhi/.openclaw/workspace/stack-2.9/data/rtmp-tools" | |
| def get_tool_description(tool_name: str) -> str: | |
| """Get tool descriptions from tool names""" | |
| descriptions = { | |
| "BashTool": "Execute shell commands in a sandboxed environment", | |
| "FileReadTool": "Read file contents from the filesystem", | |
| "FileWriteTool": "Write content to files", | |
| "FileEditTool": "Edit files using sed-style replacements", | |
| "GlobTool": "Find files matching glob patterns", | |
| "GrepTool": "Search for patterns in files", | |
| "TaskCreateTool": "Create tasks in the task list", | |
| "TaskListTool": "List all tasks in the task list", | |
| "TaskUpdateTool": "Update task status and details", | |
| "TaskGetTool": "Get details of a specific task", | |
| "WebSearchTool": "Search the web for information", | |
| "WebFetchTool": "Fetch and analyze web pages", | |
| "SkillTool": "Execute user-invocable skills", | |
| "McpTool": "Call MCP (Model Context Protocol) tools", | |
| "AgentTool": "Delegate tasks to sub-agents", | |
| } | |
| return descriptions.get(tool_name.replace("Tool", ""), f"Tool: {tool_name}") | |
| def extract_tool_examples(): | |
| """Extract tool patterns and create training examples""" | |
| examples = [] | |
| tools_dir = Path(RTMP_DIR) / "tools" | |
| if not tools_dir.exists(): | |
| print(f"❌ Tools directory not found: {tools_dir}") | |
| return examples | |
| # Process each tool | |
| tool_dirs = [d for d in tools_dir.iterdir() if d.is_dir() and d.name.endswith("Tool")] | |
| for tool_dir in tool_dirs: | |
| tool_name = tool_dir.name | |
| # Get main tool file | |
| main_files = list(tool_dir.glob("*.tsx")) + list(tool_dir.glob("*.ts")) | |
| # Create example for this tool | |
| desc = get_tool_description(tool_name) | |
| # Generate prompt-response pairs for tool usage | |
| example = { | |
| "messages": [ | |
| { | |
| "role": "user", | |
| "content": f"How do I use the {tool_name}? What does it do?" | |
| }, | |
| { | |
| "role": "assistant", | |
| "content": f"The {tool_name} allows you to {desc.lower()}.\n\n" + | |
| f"This is one of the core tools in RTMP (Real-Time AI Assistant).\n\n" + | |
| f"Tools like {tool_name} enable the assistant to interact with the filesystem, " + | |
| f"execute commands, manage tasks, and more." | |
| } | |
| ] | |
| } | |
| examples.append(example) | |
| # Add more specific examples for common tools | |
| if "Bash" in tool_name: | |
| example = { | |
| "messages": [ | |
| {"role": "user", "content": "List all files in the current directory"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "Bash", "arguments": {"command": "ls -la"}} | |
| ]} | |
| ] | |
| } | |
| examples.append(example) | |
| elif "FileRead" in tool_name: | |
| example = { | |
| "messages": [ | |
| {"role": "user", "content": "Read the contents of config.json"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "FileRead", "arguments": {"file_path": "config.json"}} | |
| ]} | |
| ] | |
| } | |
| examples.append(example) | |
| elif "Glob" in tool_name: | |
| example = { | |
| "messages": [ | |
| {"role": "user", "content": "Find all TypeScript files in the project"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "Glob", "arguments": {"pattern": "**/*.ts"}} | |
| ]} | |
| ] | |
| } | |
| examples.append(example) | |
| elif "Grep" in tool_name: | |
| example = { | |
| "messages": [ | |
| {"role": "user", "content": "Find all occurrences of 'TODO' in the code"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "Grep", "arguments": {"pattern": "TODO", "path": "."}} | |
| ]} | |
| ] | |
| } | |
| examples.append(example) | |
| elif "TaskCreate" in tool_name: | |
| example = { | |
| "messages": [ | |
| {"role": "user", "content": "Create a task to fix the login bug"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "TaskCreate", "arguments": { | |
| "subject": "Fix login bug", | |
| "description": "Investigate and fix the login issue" | |
| }} | |
| ]} | |
| ] | |
| } | |
| examples.append(example) | |
| elif "WebSearch" in tool_name: | |
| example = { | |
| "messages": [ | |
| {"role": "user", "content": "Search for latest Python 3.14 features"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "WebSearch", "arguments": {"query": "Python 3.14 new features"}} | |
| ]} | |
| ] | |
| } | |
| examples.append(example) | |
| return examples | |
| def main(): | |
| print("=" * 60) | |
| print("Extracting RTMP Tool Patterns for Training") | |
| print("=" * 60) | |
| # Create output directory | |
| os.makedirs(OUTPUT_DIR, exist_ok=True) | |
| # Extract examples | |
| examples = extract_tool_examples() | |
| print(f"\n✅ Extracted {len(examples)} tool usage examples") | |
| # Save to JSONL | |
| output_file = os.path.join(OUTPUT_DIR, "tool_patterns.jsonl") | |
| with open(output_file, 'w') as f: | |
| for ex in examples: | |
| f.write(json.dumps(ex) + '\n') | |
| print(f"✅ Saved to: {output_file}") | |
| # Also show some examples | |
| print("\n📋 Sample examples:") | |
| for i, ex in enumerate(examples[:3]): | |
| user_msg = ex["messages"][0]["content"] | |
| print(f" {i+1}. User: {user_msg[:60]}...") | |
| if __name__ == "__main__": | |
| main() |