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
File size: 3,274 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 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | """TaskGetTool - Get details of a specific task for Stack 2.9"""
import json
from pathlib import Path
from .base import BaseTool, ToolResult
from .registry import tool_registry
TASKS_FILE = Path.home() / ".stack-2.9" / "tasks.json"
def _load_tasks() -> dict:
"""Load tasks from disk."""
TASKS_FILE.parent.mkdir(parents=True, exist_ok=True)
if TASKS_FILE.exists():
return json.loads(TASKS_FILE.read_text())
return {"tasks": []}
class TaskGetTool(BaseTool):
"""Get details of a specific task."""
name = "task_get"
description = "Get details of a task by ID"
input_schema = {
"type": "object",
"properties": {
"task_id": {"type": "string", "description": "Task ID"}
},
"required": ["task_id"]
}
async def execute(self, task_id: str) -> ToolResult:
"""Get task details."""
data = _load_tasks()
for task in data.get("tasks", []):
if task.get("id") == task_id:
return ToolResult(success=True, data={
"task": task,
"found": True
})
return ToolResult(success=False, error=f"Task {task_id} not found")
class TaskOutputTool(BaseTool):
"""Get output from a completed task."""
name = "task_output"
description = "Get output from a completed task"
input_schema = {
"type": "object",
"properties": {
"task_id": {"type": "string", "description": "Task ID"}
},
"required": ["task_id"]
}
async def execute(self, task_id: str) -> ToolResult:
"""Get task output."""
data = _load_tasks()
for task in data.get("tasks", []):
if task.get("id") == task_id:
output = task.get("output") or task.get("result")
status = task.get("status", "unknown")
return ToolResult(success=True, data={
"task_id": task_id,
"status": status,
"output": output,
"has_output": output is not None
})
return ToolResult(success=False, error=f"Task {task_id} not found")
class TaskStopTool(BaseTool):
"""Stop a running task."""
name = "task_stop"
description = "Stop a running or pending task"
input_schema = {
"type": "object",
"properties": {
"task_id": {"type": "string", "description": "Task ID to stop"}
},
"required": ["task_id"]
}
async def execute(self, task_id: str) -> ToolResult:
"""Stop task."""
data = _load_tasks()
for task in data.get("tasks", []):
if task.get("id") == task_id:
old_status = task.get("status")
task["status"] = "stopped"
task["stopped_at"] = datetime.now().isoformat()
TASKS_FILE.write_text(json.dumps(data, indent=2))
return ToolResult(success=True, data={
"task_id": task_id,
"old_status": old_status,
"new_status": "stopped"
})
return ToolResult(success=False, error=f"Task {task_id} not found")
from datetime import datetime
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