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: 3,941 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | """TeamDeleteTool - Delete/disband a team for Stack 2.9"""
import json
from datetime import datetime
from pathlib import Path
from typing import Any, Dict
from .base import BaseTool, ToolResult
from .registry import tool_registry
TEAMS_FILE = Path.home() / ".stack-2.9" / "teams.json"
def _load_teams() -> Dict[str, Any]:
"""Load teams from disk."""
TEAMS_FILE.parent.mkdir(parents=True, exist_ok=True)
if TEAMS_FILE.exists():
return json.loads(TEAMS_FILE.read_text())
return {"teams": []}
def _save_teams(data: Dict[str, Any]) -> None:
"""Save teams to disk."""
TEAMS_FILE.write_text(json.dumps(data, indent=2))
class TeamDeleteTool(BaseTool):
"""Delete or disband a team."""
name = "team_delete"
description = "Delete and disband a team"
input_schema = {
"type": "object",
"properties": {
"team_id": {"type": "string", "description": "Team ID to delete"},
"force": {"type": "boolean", "default": False, "description": "Force delete even if tasks pending"}
},
"required": ["team_id"]
}
async def execute(self, team_id: str, force: bool = False) -> ToolResult:
"""Delete team."""
data = _load_teams()
# Find team
team = None
for t in data.get("teams", []):
if t.get("id") == team_id:
team = t
break
if not team:
return ToolResult(success=False, error=f"Team {team_id} not found")
# Check for pending tasks
if not force and team.get("status") == "active":
pending_tasks = [a for a in team.get("agents", []) if a.get("status") == "active"]
if pending_tasks:
return ToolResult(success=False, error=f"Team has {len(pending_tasks)} active agents. Use force=true to delete anyway.")
# Archive team before deletion
archive_dir = Path.home() / ".stack-2.9" / "archives"
archive_dir.mkdir(parents=True, exist_ok=True)
archive_file = archive_dir / f"team_{team_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
archive_file.write_text(json.dumps(team, indent=2))
# Remove from teams list
data["teams"] = [t for t in data["teams"] if t.get("id") != team_id]
_save_teams(data)
return ToolResult(success=True, data={
"team_id": team_id,
"team_name": team.get("name"),
"status": "deleted",
"archived_to": str(archive_file)
})
class TeamLeaveTool(BaseTool):
"""Leave a team (for agents)."""
name = "team_leave"
description = "Leave a team"
input_schema = {
"type": "object",
"properties": {
"team_id": {"type": "string", "description": "Team ID"},
"agent_name": {"type": "string", "description": "Agent name to remove"}
},
"required": ["team_id", "agent_name"]
}
async def execute(self, team_id: str, agent_name: str) -> ToolResult:
"""Leave team."""
data = _load_teams()
for team in data.get("teams", []):
if team.get("id") == team_id:
agents = team.get("agents", [])
original_count = len(agents)
agents = [a for a in agents if a.get("name") != agent_name]
if len(agents) == original_count:
return ToolResult(success=False, error=f"Agent {agent_name} not found in team")
team["agents"] = agents
_save_teams(data)
return ToolResult(success=True, data={
"team_id": team_id,
"agent_removed": agent_name,
"status": "removed"
})
return ToolResult(success=False, error=f"Team {team_id} not found")
# Register tools
tool_registry.register(TeamDeleteTool())
tool_registry.register(TeamLeaveTool())
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