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: 6,617 Bytes
30d572f | 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 | #!/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() |