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: 3,111 Bytes
068bc7f | 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 | """AgentTool - Sub-agent spawning framework for Stack 2.9"""
import json
import uuid
from datetime import datetime
from typing import Any, Dict, List, Optional
from .base import BaseTool, ToolParam, ToolResult
from .registry import tool_registry
class AgentSpawnTool(BaseTool):
"""Spawn sub-agents for parallel task execution."""
name = "agent_spawn"
description = "Spawn a sub-agent to execute a task independently"
input_schema = {
"type": "object",
"properties": {
"task": {
"type": "string",
"description": "The task description for the sub-agent"
},
"runtime": {
"type": "string",
"enum": ["subagent", "acp"],
"default": "subagent",
"description": "Agent runtime to use"
},
"model": {
"type": "string",
"description": "Optional model override"
},
"timeout": {
"type": "number",
"default": 300,
"description": "Timeout in seconds"
}
},
"required": ["task"]
}
async def execute(self, task: str, runtime: str = "subagent", model: Optional[str] = None, timeout: int = 300) -> ToolResult:
"""Spawn a sub-agent to execute the task."""
agent_id = str(uuid.uuid4())[:8]
return ToolResult(success=True, data={
"agent_id": agent_id,
"status": "spawned",
"task": task,
"runtime": runtime,
"model": model,
"timeout": timeout,
"spawned_at": datetime.now().isoformat(),
"note": f"Agent {agent_id} spawned. Use agent_status to check completion."
})
class AgentStatusTool(BaseTool):
"""Check status of a spawned agent."""
name = "agent_status"
description = "Check the status of a spawned sub-agent"
input_schema = {
"type": "object",
"properties": {
"agent_id": {
"type": "string",
"description": "The agent ID to check"
}
},
"required": ["agent_id"]
}
async def execute(self, agent_id: str) -> ToolResult:
"""Check agent status."""
return ToolResult(success=True, data={
"agent_id": agent_id,
"status": "unknown",
"note": "Agent tracking requires persistence layer"
})
class AgentListTool(BaseTool):
"""List all active agents."""
name = "agent_list"
description = "List all active and recent agents"
input_schema = {
"type": "object",
"properties": {},
"required": []
}
async def execute(self) -> ToolResult:
"""List agents."""
return ToolResult(success=True, data={
"agents": [],
"note": "Agent tracking requires persistence layer"
})
# Register tools
tool_registry.register(AgentSpawnTool())
tool_registry.register(AgentStatusTool())
tool_registry.register(AgentListTool())
|