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 Phase 2 RTMP tools (web_fetch, messaging, remote_trigger, tool_discovery)
e6853d3 | """WebFetchTool - Web content fetching and parsing for Stack 2.9""" | |
| import re | |
| from datetime import datetime | |
| from typing import Any, Dict, List, Optional | |
| from urllib.parse import urlparse | |
| try: | |
| import httpx | |
| HAS_HTTPX = True | |
| except ImportError: | |
| HAS_HTTPX = False | |
| from .base import BaseTool, ToolResult | |
| from .registry import tool_registry | |
| def _extract_readable_content(html: str) -> str: | |
| """Extract readable text from HTML.""" | |
| # Remove scripts and styles | |
| text = re.sub(r'<script[^>]*>.*?</script>', '', html, flags=re.DOTALL | re.IGNORECASE) | |
| text = re.sub(r'<style[^>]*>.*?</style>', '', text, flags=re.DOTALL | re.IGNORECASE) | |
| # Remove HTML tags | |
| text = re.sub(r'<[^>]+>', ' ', text) | |
| # Clean whitespace | |
| text = re.sub(r'\s+', ' ', text).strip() | |
| return text | |
| class WebFetchTool(BaseTool): | |
| """Fetch and extract readable content from URLs.""" | |
| name = "web_fetch" | |
| description = "Fetch web page content and extract readable text" | |
| input_schema = { | |
| "type": "object", | |
| "properties": { | |
| "url": { | |
| "type": "string", | |
| "description": "URL to fetch" | |
| }, | |
| "max_chars": { | |
| "type": "number", | |
| "default": 10000, | |
| "description": "Maximum characters to return" | |
| }, | |
| "extract_links": { | |
| "type": "boolean", | |
| "default": False, | |
| "description": "Extract links from the page" | |
| } | |
| }, | |
| "required": ["url"] | |
| } | |
| async def execute(self, url: str, max_chars: int = 10000, extract_links: bool = False) -> ToolResult: | |
| """Fetch URL content.""" | |
| if not HAS_HTTPX: | |
| return ToolResult(success=False, error="httpx library not installed") | |
| parsed = urlparse(url) | |
| if not parsed.scheme: | |
| return ToolResult(success=False, error="Invalid URL - missing scheme") | |
| try: | |
| response = httpx.get(url, timeout=15.0, follow_redirects=True) | |
| response.raise_for_status() | |
| content_type = response.headers.get("content-type", "") | |
| if "text/html" not in content_type and "text/plain" not in content_type: | |
| return ToolResult(success=True, data={ | |
| "url": url, | |
| "content": response.text[:max_chars], | |
| "content_type": content_type, | |
| "status_code": response.status_code | |
| }) | |
| text = _extract_readable_content(response.text) | |
| text = text[:max_chars] | |
| result = { | |
| "url": url, | |
| "content": text, | |
| "content_type": content_type, | |
| "status_code": response.status_code, | |
| "fetched_at": datetime.now().isoformat() | |
| } | |
| if extract_links: | |
| links = re.findall(r'href=["\']([^"\']+)["\']', response.text) | |
| result["links"] = links[:50] | |
| return ToolResult(success=True, data=result) | |
| except httpx.TimeoutException: | |
| return ToolResult(success=False, error=f"Timeout fetching {url}") | |
| except httpx.HTTPError as e: | |
| return ToolResult(success=False, error=f"HTTP error: {e}") | |
| except Exception as e: | |
| return ToolResult(success=False, error=str(e)) | |
| class WebFetchMetaTool(BaseTool): | |
| """Get metadata from a URL without full content.""" | |
| name = "web_fetch_meta" | |
| description = "Get metadata (title, description, images) from a URL" | |
| input_schema = { | |
| "type": "object", | |
| "properties": { | |
| "url": { | |
| "type": "string", | |
| "description": "URL to analyze" | |
| } | |
| }, | |
| "required": ["url"] | |
| } | |
| async def execute(self, url: str) -> ToolResult: | |
| """Get URL metadata.""" | |
| if not HAS_HTTPX: | |
| return ToolResult(success=False, error="httpx library not installed") | |
| try: | |
| response = httpx.get(url, timeout=10.0, follow_redirects=True) | |
| response.raise_for_status() | |
| title = re.search(r'<title[^>]*>([^<]+)</title>', response.text, re.IGNORECASE) | |
| description = re.search(r'<meta[^>]+name=["\']description["\'][^>]+content=["\']([^"\']+)["\']', response.text, re.IGNORECASE) | |
| og_image = re.search(r'<meta[^>]+property=["\']og:image["\'][^>]+content=["\']([^"\']+)["\']', response.text, re.IGNORECASE) | |
| return ToolResult(success=True, data={ | |
| "url": url, | |
| "title": title.group(1).strip() if title else None, | |
| "description": description.group(1).strip() if description else None, | |
| "og_image": og_image.group(1).strip() if og_image else None, | |
| "status_code": response.status_code | |
| }) | |
| except Exception as e: | |
| return ToolResult(success=False, error=str(e)) | |
| # Register tools | |
| tool_registry.register(WebFetchTool()) | |
| tool_registry.register(WebFetchMetaTool()) | |