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: 2,911 Bytes
5dc5419 | 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 | """BriefTool - Generate briefings for Stack 2.9"""
import json
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Optional
from .base import BaseTool, ToolResult
from .registry import tool_registry
class BriefTool(BaseTool):
"""Generate a briefing for a task."""
name = "brief"
description = "Generate a structured briefing for a task"
input_schema = {
"type": "object",
"properties": {
"task": {"type": "string", "description": "Main task or goal"},
"context": {"type": "string", "description": "Additional context"},
"constraints": {"type": "array", "items": {"type": "string"}, "description": "Constraints or requirements"},
"hints": {"type": "array", "items": {"type": "string"}, "description": " Helpful hints"},
"format": {"type": "string", "enum": ["concise", "detailed"], "default": "concise"}
},
"required": ["task"]
}
async def execute(self, task: str, context: Optional[str] = None, constraints: Optional[List[str]] = None, hints: Optional[List[str]] = None, format: str = "concise") -> ToolResult:
"""Generate brief."""
brief_id = f"brief_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
sections = {
"id": brief_id,
"task": task,
"created_at": datetime.now().isoformat()
}
if context:
sections["context"] = context
if constraints:
sections["constraints"] = constraints
if hints:
sections["hints"] = hints
if format == "detailed":
sections["format_version"] = "detailed"
sections["priority"] = "medium"
sections["estimated_complexity"] = "unknown"
else:
sections["format_version"] = "concise"
return ToolResult(success=True, data=sections)
class BriefSummaryTool(BaseTool):
"""Summarize a previous brief or conversation."""
name = "brief_summary"
description = "Generate a summary briefing"
input_schema = {
"type": "object",
"properties": {
"content": {"type": "string", "description": "Content to summarize"},
"max_points": {"type": "number", "default": 5, "description": "Maximum key points"}
},
"required": ["content"]
}
async def execute(self, content: str, max_points: int = 5) -> ToolResult:
"""Generate summary."""
# Simple extractive summarization
lines = [l.strip() for l in content.split('\n') if l.strip()]
points = lines[:max_points]
return ToolResult(success=True, data={
"summary": points,
"total_lines": len(lines),
"points_extracted": len(points)
})
# Register tools
tool_registry.register(BriefTool())
tool_registry.register(BriefSummaryTool())
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