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: 3,803 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 | """FileWriteTool - Write content to files for Stack 2.9"""
import os
from datetime import datetime
from pathlib import Path
from .base import BaseTool, ToolResult
from .registry import tool_registry
BACKUP_DIR = Path.home() / ".stack-2.9" / "backups"
class FileWriteTool(BaseTool):
"""Write content to a file."""
name = "file_write"
description = "Write content to a file"
input_schema = {
"type": "object",
"properties": {
"path": {"type": "string", "description": "File path to write"},
"content": {"type": "string", "description": "Content to write"},
"append": {"type": "boolean", "default": False, "description": "Append instead of overwrite"},
"create_backup": {"type": "boolean", "default": True, "description": "Create backup if file exists"}
},
"required": ["path", "content"]
}
async def execute(self, path: str, content: str, append: bool = False, create_backup: bool = True) -> ToolResult:
"""Write file."""
file_path = Path(path)
# Create parent directories if needed
file_path.parent.mkdir(parents=True, exist_ok=True)
backup_path = None
# Backup existing file
if file_path.exists() and create_backup and not append:
BACKUP_DIR.mkdir(parents=True, exist_ok=True)
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
backup_name = f"{file_path.name}.{timestamp}.bak"
backup_path = BACKUP_DIR / backup_name
backup_path.write_text(file_path.read_text())
# Write content
try:
if append:
existing = file_path.read_text() if file_path.exists() else ""
file_path.write_text(existing + content)
else:
file_path.write_text(content)
except Exception as e:
return ToolResult(success=False, error=f"Cannot write file: {e}")
return ToolResult(success=True, data={
"path": str(file_path),
"bytes_written": len(content),
"backup": str(backup_path) if backup_path else None,
"mode": "append" if append else "overwrite"
})
class FileDeleteTool(BaseTool):
"""Delete a file."""
name = "file_delete"
description = "Delete a file"
input_schema = {
"type": "object",
"properties": {
"path": {"type": "string", "description": "File path to delete"},
"create_backup": {"type": "boolean", "default": True}
},
"required": ["path"]
}
async def execute(self, path: str, create_backup: bool = True) -> ToolResult:
"""Delete file."""
file_path = Path(path)
if not file_path.exists():
return ToolResult(success=False, error=f"File not found: {path}")
if not file_path.is_file():
return ToolResult(success=False, error=f"Not a file: {path}")
backup_path = None
# Backup before delete
if create_backup:
BACKUP_DIR.mkdir(parents=True, exist_ok=True)
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
backup_name = f"{file_path.name}.{timestamp}.deleted.bak"
backup_path = BACKUP_DIR / backup_name
backup_path.write_text(file_path.read_text())
try:
file_path.unlink()
except Exception as e:
return ToolResult(success=False, error=f"Cannot delete file: {e}")
return ToolResult(success=True, data={
"path": str(file_path),
"deleted": True,
"backup": str(backup_path) if backup_path else None
})
# Register tools
tool_registry.register(FileWriteTool())
tool_registry.register(FileDeleteTool())
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