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", device_map="auto") 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: 3,663 Bytes
3de42e7 b8e3e42 3de42e7 b8e3e42 3de42e7 5ddf5f9 3de42e7 c7f1596 3de42e7 | 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 | """MCP Server for Stack 2.9 - Exposes Stack tools via Model Context Protocol"""
import asyncio
import os
import sys
from typing import Any
# Ensure project root is on the path so 'from src.tools import' works
_project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
if _project_root not in sys.path:
sys.path.insert(0, _project_root)
from mcp.server.fastmcp import FastMCP
# Import all Stack 2.9 tools (triggers auto-registration)
from src.tools import (
BaseTool,
ToolResult,
get_registry,
file_read,
grep_tool,
task_management,
team_tool,
agent_tool,
)
def _tool_to_mcp(tool: BaseTool) -> dict[str, Any]:
"""Convert a Stack 2.9 tool to MCP tool schema."""
schema = tool.input_schema
if callable(schema):
schema = schema()
return {
"name": tool.name,
"description": tool.description,
"inputSchema": schema,
}
def _call_tool_sync(tool: BaseTool, arguments: dict[str, Any]) -> Any:
"""Call a tool and extract result data, handling sync/async execute."""
import inspect
execute = tool.execute
# Determine if execute is async or sync
if inspect.iscoroutinefunction(execute):
# Run in event loop
loop = asyncio.get_event_loop()
if inspect.iscoroutinefunction(execute):
result = loop.run_until_complete(execute(**arguments))
else:
result = execute(**arguments)
else:
# Sync execute (uses input_data dict style)
if hasattr(tool, 'input_schema') and not callable(tool.input_schema):
result = execute(arguments)
else:
result = execute(**arguments)
if isinstance(result, ToolResult):
if result.success:
return {"success": True, "data": result.data}
else:
return {"success": False, "error": result.error}
return result
def _register_tool(mcp: FastMCP, tool: BaseTool) -> None:
"""Register a single Stack tool as an MCP tool."""
tool_name = tool.name
schema = tool.input_schema
if callable(schema):
schema = schema()
async def handler(arguments: dict[str, Any]) -> dict[str, Any]:
return _call_tool_sync(tool, arguments)
mcp.add_tool(handler, name=tool_name, description=tool.description)
def _register_all_tools(mcp: FastMCP) -> int:
"""Register all tools from the Stack 2.9 registry."""
registry = get_registry()
count = 0
for tool in registry._tools.values():
try:
_register_tool(mcp, tool)
count += 1
except Exception as e:
print(f"Failed to register tool {getattr(tool, 'name', 'unknown')}: {e}")
return count
# Create the FastMCP server
mcp = FastMCP("Stack2.9")
def main():
"""Main entry point - register tools and run the server."""
# Import all tools to ensure registration
from src.tools import (
agent_tool,
ask_question,
brief_tool,
config_tool,
file_edit,
file_read,
file_write,
glob_tool,
grep_tool,
messaging,
plan_mode,
remote_trigger,
scheduling,
skill_tool,
sleep_tool,
synthetic_output,
task_get,
task_management,
team_delete,
team_tool,
todo_tool,
tool_discovery,
web_fetch,
web_search,
worktree_tool,
)
# Register all tools from registry
count = _register_all_tools(mcp)
print(f"Registered {count} Stack 2.9 tools as MCP tools")
# Run the MCP server
mcp.run()
if __name__ == "__main__":
main() |