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
walidsobhie-code
feat: Add remaining RTMP tools (FileRead, FileWrite, Sleep, AskQuestion, Brief, TaskGet, TeamDelete, MCPTool, Worktree, SyntheticOutput)
5dc5419 | """SyntheticOutputTool - Generate structured synthetic output for Stack 2.9""" | |
| import json | |
| from datetime import datetime | |
| from typing import Any, Dict, List, Optional | |
| from .base import BaseTool, ToolResult | |
| from .registry import tool_registry | |
| class SyntheticOutputTool(BaseTool): | |
| """Generate structured synthetic output.""" | |
| name = "synthetic_output" | |
| description = "Generate structured synthetic output in various formats" | |
| input_schema = { | |
| "type": "object", | |
| "properties": { | |
| "output_type": { | |
| "type": "string", | |
| "enum": ["json", "markdown", "text", "html"], | |
| "description": "Output format" | |
| }, | |
| "content": {"type": "string", "description": "Content to format"}, | |
| "metadata": {"type": "object", "description": "Optional metadata"} | |
| }, | |
| "required": ["output_type", "content"] | |
| } | |
| async def execute(self, output_type: str, content: str, metadata: Optional[Dict] = None) -> ToolResult: | |
| """Generate output.""" | |
| result = { | |
| "type": output_type, | |
| "content": content, | |
| "generated_at": datetime.now().isoformat() | |
| } | |
| if metadata: | |
| result["metadata"] = metadata | |
| if output_type == "json": | |
| formatted = json.dumps(result, indent=2) | |
| elif output_type == "markdown": | |
| formatted = self._to_markdown(result) | |
| elif output_type == "html": | |
| formatted = self._to_html(result) | |
| else: | |
| formatted = content | |
| return ToolResult(success=True, data={ | |
| "type": output_type, | |
| "formatted": formatted, | |
| "raw": result | |
| }) | |
| def _to_markdown(self, data: Dict) -> str: | |
| """Convert to markdown.""" | |
| lines = [f"# Synthetic Output", f"**Type:** {data.get('type')}", f"**Generated:** {data.get('generated_at')}", ""] | |
| if data.get("metadata"): | |
| lines.append("## Metadata") | |
| for k, v in data["metadata"].items(): | |
| lines.append(f"- **{k}:** {v}") | |
| lines.append("") | |
| lines.append("## Content") | |
| lines.append(data.get("content", "")) | |
| return '\n'.join(lines) | |
| def _to_html(self, data: Dict) -> str: | |
| """Convert to HTML.""" | |
| meta = "" | |
| if data.get("metadata"): | |
| meta = "<ul>" + "".join(f"<li><strong>{k}:</strong> {v}</li>" for k, v in data["metadata"].items()) + "</ul>" | |
| return f""" | |
| <div class="synthetic-output"> | |
| <h2>Synthetic Output</h2> | |
| <p><strong>Type:</strong> {data.get('type')}</p> | |
| <p><strong>Generated:</strong> {data.get('generated_at')}</p> | |
| {meta} | |
| <div class="content"> | |
| <pre>{data.get('content', '')}</pre> | |
| </div> | |
| </div> | |
| """ | |
| class StructuredDataTool(BaseTool): | |
| """Convert unstructured data to structured format.""" | |
| name = "structure_data" | |
| description = "Convert unstructured data to structured format" | |
| input_schema = { | |
| "type": "object", | |
| "properties": { | |
| "data": {"type": "string", "description": "Data to structure"}, | |
| "schema": {"type": "object", "description": "Target schema"}, | |
| "format": {"type": "string", "enum": ["json", "csv"], "default": "json"} | |
| }, | |
| "required": ["data"] | |
| } | |
| async def execute(self, data: str, schema: Optional[Dict] = None, format: str = "json") -> ToolResult: | |
| """Structure data.""" | |
| # Simple JSON detection | |
| try: | |
| parsed = json.loads(data) | |
| return ToolResult(success=True, data={ | |
| "parsed": parsed, | |
| "format": "json", | |
| "structured": True | |
| }) | |
| except json.JSONDecodeError: | |
| pass | |
| # Treat as plain text | |
| return ToolResult(success=True, data={ | |
| "data": data, | |
| "format": "text", | |
| "structured": False, | |
| "note": "Could not auto-structure, returned as text" | |
| }) | |
| # Register tools | |
| tool_registry.register(SyntheticOutputTool()) | |
| tool_registry.register(StructuredDataTool()) | |