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 advanced training data from RTMP tools | |
| Includes actual patterns, best practices, and tool definitions | |
| """ | |
| 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_definitions(): | |
| """Get detailed tool definitions with examples""" | |
| tools = [] | |
| # BashTool | |
| tools.append({ | |
| "messages": [ | |
| {"role": "system", "content": "You are a helpful AI coding assistant with access to various tools."}, | |
| {"role": "user", "content": "List all files in the current directory including hidden files"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "Bash", "arguments": {"command": "ls -la"}} | |
| ]}, | |
| {"role": "tool", "content": "total 128\ndrwxr-xr-x 1 user staff 4096 Apr 4 10:00 .\ndrwxr-xr-x 1 user staff 4096 Apr 4 10:00 ..\n-rw-r--r-- 1 user staff 1024 Apr 4 09:00 file.ts"} | |
| ] | |
| }) | |
| tools.append({ | |
| "messages": [ | |
| {"role": "user", "content": "Find all TypeScript files in the src directory"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "Glob", "arguments": {"pattern": "src/**/*.ts"}} | |
| ]} | |
| ] | |
| }) | |
| tools.append({ | |
| "messages": [ | |
| {"role": "user", "content": "Search for all TODO comments in the codebase"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "Grep", "arguments": {"pattern": "TODO", "path": ".", "output_mode": "content"}} | |
| ]} | |
| ] | |
| }) | |
| tools.append({ | |
| "messages": [ | |
| {"role": "user", "content": "Read the package.json file"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "FileRead", "arguments": {"file_path": "package.json"}} | |
| ]} | |
| ] | |
| }) | |
| tools.append({ | |
| "messages": [ | |
| {"role": "user", "content": "Create a new task to implement the login feature"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "TaskCreate", "arguments": { | |
| "subject": "Implement login feature", | |
| "description": "Add user authentication with OAuth", | |
| "activeForm": "Implementing login feature" | |
| }} | |
| ]} | |
| ] | |
| }) | |
| tools.append({ | |
| "messages": [ | |
| {"role": "user", "content": "Search the web for how to use PyTorch MPS on Apple Silicon"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "WebSearch", "arguments": {"query": "PyTorch MPS Apple Silicon tutorial"}} | |
| ]} | |
| ] | |
| }) | |
| tools.append({ | |
| "messages": [ | |
| {"role": "user", "content": "Fetch the documentation from https://pytorch.org/docs"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "WebFetch", "arguments": {"url": "https://pytorch.org/docs", "prompt": "Extract the main features of PyTorch"}} | |
| ]} | |
| ] | |
| }) | |
| # Task management examples | |
| tools.append({ | |
| "messages": [ | |
| {"role": "user", "content": "Show me all open tasks"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "TaskList", "arguments": {}} | |
| ]} | |
| ] | |
| }) | |
| tools.append({ | |
| "messages": [ | |
| {"role": "user", "content": "What's the status of the login feature task?"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "TaskGet", "arguments": {"taskId": "1"}} | |
| ]} | |
| ] | |
| }) | |
| # File operations | |
| tools.append({ | |
| "messages": [ | |
| {"role": "user", "content": "Write a simple Python script to a new file"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "FileWrite", "arguments": { | |
| "file_path": "hello.py", | |
| "content": "print('Hello, World!')\n" | |
| }} | |
| ]} | |
| ] | |
| }) | |
| tools.append({ | |
| "messages": [ | |
| {"role": "user", "content": "Edit the config file to enable debug mode"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "FileEdit", "arguments": { | |
| "file_path": "config.json", | |
| "old_string": "\"debug\": false", | |
| "new_string": "\"debug\": true" | |
| }} | |
| ]} | |
| ] | |
| }) | |
| # Complex multi-step examples | |
| tools.append({ | |
| "messages": [ | |
| {"role": "user", "content": "First find all React component files, then read one of them"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "Glob", "arguments": {"pattern": "**/*.{tsx,jsx}"}}, | |
| {"name": "TaskCreate", "arguments": { | |
| "subject": "Read React components", | |
| "description": "Read the found component files", | |
| "activeForm": "Reading React components" | |
| }} | |
| ]} | |
| ] | |
| }) | |
| # Skill invocation | |
| tools.append({ | |
| "messages": [ | |
| {"role": "user", "content": "Commit the changes with a message"}, | |
| {"role": "assistant", "tool_calls": [ | |
| {"name": "Skill", "arguments": {"skill": "git-commit", "args": "-m 'Fix bug'"}} | |
| ]} | |
| ] | |
| }) | |
| return tools | |
| def main(): | |
| print("=" * 60) | |
| print("Extracting Advanced RTMP Tool Patterns") | |
| print("=" * 60) | |
| # Get tool examples | |
| tools = get_tool_definitions() | |
| print(f"\n✅ Created {len(tools)} advanced tool examples") | |
| # Save to JSONL | |
| output_file = os.path.join(OUTPUT_DIR, "advanced_tool_patterns.jsonl") | |
| with open(output_file, 'w') as f: | |
| for ex in tools: | |
| f.write(json.dumps(ex) + '\n') | |
| print(f"✅ Saved to: {output_file}") | |
| # Combine with previous | |
| prev_file = os.path.join(OUTPUT_DIR, "tool_patterns.jsonl") | |
| combined_file = os.path.join(OUTPUT_DIR, "combined_tools.jsonl") | |
| with open(combined_file, 'w') as out: | |
| # Previous simple patterns | |
| if os.path.exists(prev_file): | |
| with open(prev_file) as f: | |
| for line in f: | |
| out.write(line) | |
| # Advanced patterns | |
| with open(output_file) as f: | |
| for line in f: | |
| out.write(line) | |
| print(f"\n📦 Total combined examples:") | |
| with open(combined_file) as f: | |
| count = sum(1 for _ in f) | |
| print(f" {count} tool usage examples") | |
| if __name__ == "__main__": | |
| main() |