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 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
walidsobhie-code
feat: Add remaining RTMP tools (FileRead, FileWrite, Sleep, AskQuestion, Brief, TaskGet, TeamDelete, MCPTool, Worktree, SyntheticOutput)
5dc5419 | """MCPTool - MCP protocol tool integration for Stack 2.9""" | |
| import json | |
| from pathlib import Path | |
| from typing import Any, Dict, Optional | |
| from .base import BaseTool, ToolResult | |
| from .registry import tool_registry | |
| MCP_CONFIG_FILE = Path.home() / ".stack-2.9" / "mcp_config.json" | |
| def _load_mcp_config() -> Dict[str, Any]: | |
| """Load MCP configuration.""" | |
| MCP_CONFIG_FILE.parent.mkdir(parents=True, exist_ok=True) | |
| if MCP_CONFIG_FILE.exists(): | |
| return json.loads(MCP_CONFIG_FILE.read_text()) | |
| return {"servers": {}} | |
| class MCPTool(BaseTool): | |
| """Call an MCP server tool.""" | |
| name = "mcp_call" | |
| description = "Call a tool on an MCP server" | |
| input_schema = { | |
| "type": "object", | |
| "properties": { | |
| "server_name": {"type": "string", "description": "MCP server name"}, | |
| "tool_name": {"type": "string", "description": "Tool to call on server"}, | |
| "arguments": {"type": "object", "description": "Arguments for the tool"} | |
| }, | |
| "required": ["server_name", "tool_name"] | |
| } | |
| async def execute(self, server_name: str, tool_name: str, arguments: Optional[Dict] = None) -> ToolResult: | |
| """Call MCP tool.""" | |
| config = _load_mcp_config() | |
| if server_name not in config.get("servers", {}): | |
| return ToolResult(success=False, error=f"MCP server '{server_name}' not configured") | |
| server = config["servers"][server_name] | |
| return ToolResult(success=True, data={ | |
| "server": server_name, | |
| "tool": tool_name, | |
| "arguments": arguments or {}, | |
| "status": "simulated", | |
| "note": f"MCP call to {server_name}/{tool_name} - requires MCP runtime" | |
| }) | |
| class MCPServerListTool(BaseTool): | |
| """List configured MCP servers.""" | |
| name = "mcp_list_servers" | |
| description = "List all configured MCP servers" | |
| input_schema = { | |
| "type": "object", | |
| "properties": {}, | |
| "required": [] | |
| } | |
| async def execute(self) -> ToolResult: | |
| """List MCP servers.""" | |
| config = _load_mcp_config() | |
| servers = config.get("servers", {}) | |
| return ToolResult(success=True, data={ | |
| "servers": list(servers.keys()), | |
| "count": len(servers) | |
| }) | |
| class MCPServerAddTool(BaseTool): | |
| """Add an MCP server configuration.""" | |
| name = "mcp_add_server" | |
| description = "Add an MCP server configuration" | |
| input_schema = { | |
| "type": "object", | |
| "properties": { | |
| "server_name": {"type": "string", "description": "Server name"}, | |
| "command": {"type": "string", "description": "Command to start server"}, | |
| "args": {"type": "array", "items": {"type": "string"}, "description": "Command arguments"}, | |
| "env": {"type": "object", "description": "Environment variables"} | |
| }, | |
| "required": ["server_name", "command"] | |
| } | |
| async def execute(self, server_name: str, command: str, args: Optional[list] = None, env: Optional[Dict] = None) -> ToolResult: | |
| """Add MCP server.""" | |
| config = _load_mcp_config() | |
| if "servers" not in config: | |
| config["servers"] = {} | |
| config["servers"][server_name] = { | |
| "command": command, | |
| "args": args or [], | |
| "env": env or {}, | |
| "enabled": True | |
| } | |
| MCP_CONFIG_FILE.write_text(json.dumps(config, indent=2)) | |
| return ToolResult(success=True, data={ | |
| "server": server_name, | |
| "status": "added" | |
| }) | |
| class ReadMcpResourceTool(BaseTool): | |
| """Read a resource from an MCP server.""" | |
| name = "read_mcp_resource" | |
| description = "Read a resource from an MCP server" | |
| input_schema = { | |
| "type": "object", | |
| "properties": { | |
| "server_name": {"type": "string", "description": "MCP server name"}, | |
| "resource_uri": {"type": "string", "description": "Resource URI"} | |
| }, | |
| "required": ["server_name", "resource_uri"] | |
| } | |
| async def execute(self, server_name: str, resource_uri: str) -> ToolResult: | |
| """Read MCP resource.""" | |
| config = _load_mcp_config() | |
| if server_name not in config.get("servers", {}): | |
| return ToolResult(success=False, error=f"MCP server '{server_name}' not configured") | |
| return ToolResult(success=True, data={ | |
| "server": server_name, | |
| "resource_uri": resource_uri, | |
| "status": "simulated", | |
| "note": f"Read resource {resource_uri} from {server_name}" | |
| }) | |
| # Register tools | |
| tool_registry.register(MCPTool()) | |
| tool_registry.register(MCPServerListTool()) | |
| tool_registry.register(MCPServerAddTool()) | |
| tool_registry.register(ReadMcpResourceTool()) | |