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: 2,340 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 | """SleepTool - Simple delay tool for Stack 2.9"""
import asyncio
import time
from datetime import datetime
from .base import BaseTool, ToolResult
from .registry import tool_registry
class SleepTool(BaseTool):
"""Pause execution for a specified duration."""
name = "sleep"
description = "Pause execution for a specified number of seconds"
input_schema = {
"type": "object",
"properties": {
"seconds": {"type": "number", "description": "Number of seconds to sleep"}
},
"required": ["seconds"]
}
async def execute(self, seconds: float) -> ToolResult:
"""Sleep."""
if seconds <= 0:
return ToolResult(success=False, error="Seconds must be positive")
if seconds > 3600:
return ToolResult(success=False, error="Maximum sleep is 3600 seconds (1 hour)")
start = time.time()
await asyncio.sleep(seconds)
elapsed = time.time() - start
return ToolResult(success=True, data={
"requested_seconds": seconds,
"actual_elapsed": elapsed,
"completed_at": datetime.now().isoformat()
})
class WaitForTool(BaseTool):
"""Wait for a condition to become true."""
name = "wait_for"
description = "Wait for a condition with timeout"
input_schema = {
"type": "object",
"properties": {
"seconds": {"type": "number", "description": "Maximum seconds to wait"},
"poll_interval": {"type": "number", "default": 1.0, "description": "Seconds between checks"}
},
"required": ["seconds"]
}
async def execute(self, seconds: float, poll_interval: float = 1.0) -> ToolResult:
"""Wait with polling."""
if seconds <= 0 or poll_interval <= 0:
return ToolResult(success=False, error="All values must be positive")
start = time.time()
elapsed = 0
while elapsed < seconds:
await asyncio.sleep(min(poll_interval, seconds - elapsed))
elapsed = time.time() - start
return ToolResult(success=True, data={
"waited_seconds": seconds,
"actual_elapsed": elapsed,
"timed_out": True
})
# Register tools
tool_registry.register(SleepTool())
tool_registry.register(WaitForTool())
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