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: 7,442 Bytes
2531078 b64b6b0 2531078 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 | """GlobTool - File pattern matching for Stack 2.9"""
import fnmatch
import os
import re
from pathlib import Path
from typing import Any, Dict, List, Optional
from .base import BaseTool, ToolResult
from .registry import tool_registry
# Default exclusions
DEFAULT_EXCLUDES = {
'.git', '.svn', '.hg', '__pycache__', 'node_modules', '.venv', 'venv',
'env', '.idea', '.vscode', '.DS_Store', '*.pyc', '*.pyo', '*.so',
'*.dylib', '.cache', '.pytest_cache', '.mypy_cache', 'dist', 'build',
'*.egg-info', '.tox', '.nox'
}
def _should_exclude(path: Path, exclude_patterns: List[str]) -> bool:
"""Check if path should be excluded."""
name = path.name
# Check default exclusions
if name in DEFAULT_EXCLUDES:
return True
# Check custom patterns
for pattern in exclude_patterns:
if fnmatch.fnmatch(name, pattern) or fnmatch.fnmatch(str(path), pattern):
return True
return False
def _glob_pattern_to_regex(pattern: str) -> str:
"""Convert glob pattern to regex."""
# Handle ** for recursive matching
regex_parts = []
i = 0
while i < len(pattern):
c = pattern[i]
if c == '*':
if i + 1 < len(pattern) and pattern[i + 1] == '*':
# ** matches everything including /
regex_parts.append('.*')
i += 2
else:
# * matches everything except /
regex_parts.append('[^/]*')
i += 1
elif c == '?':
regex_parts.append('.')
i += 1
elif c == '[':
# Character class
j = i + 1
if j < len(pattern) and pattern[j] == '!':
regex_parts.append('[^')
j += 1
else:
regex_parts.append('[')
while j < len(pattern) and pattern[j] != ']':
regex_parts.append(re.escape(pattern[j]))
j += 1
regex_parts.append(']')
i = j + 1
else:
regex_parts.append(re.escape(c))
i += 1
return ''.join(regex_parts)
def _match_glob(path: Path, pattern: str) -> bool:
"""Check if path matches glob pattern."""
import re
# Normalize pattern - handle **/*.py style patterns
if pattern.startswith('**/'):
# Recursive pattern
regex_pattern = _glob_pattern_to_regex(pattern)
regex = re.compile(regex_pattern)
return bool(regex.match(str(path))) or bool(regex.match(path.name))
elif '**' in pattern:
regex_pattern = _glob_pattern_to_regex(pattern)
regex = re.compile(regex_pattern)
return bool(regex.match(str(path)))
else:
# Simple pattern
return fnmatch.fnmatch(path.name, pattern) or fnmatch.fnmatch(str(path), pattern)
class GlobTool(BaseTool):
"""Find files matching glob patterns."""
name = "glob"
description = "Find files matching glob patterns"
input_schema = {
"type": "object",
"properties": {
"pattern": {"type": "string", "description": "Glob pattern (e.g., **/*.py, *.js)"},
"base_path": {"type": "string", "description": "Base directory to search"},
"exclude": {"type": "array", "items": {"type": "string"}, "description": "Patterns to exclude"},
"max_results": {"type": "number", "default": 1000, "description": "Maximum results"},
"files_only": {"type": "boolean", "default": True, "description": "Only return files"}
},
"required": ["pattern"]
}
async def execute(self, pattern: str, base_path: Optional[str] = None, exclude: Optional[List[str]] = None, max_results: int = 1000, files_only: bool = True) -> ToolResult:
"""Find files matching pattern."""
if base_path:
search_path = Path(base_path)
else:
search_path = Path.cwd()
if not search_path.exists():
return ToolResult(success=False, error=f"Path not found: {search_path}")
exclude_patterns = exclude or []
matches = []
visited_dirs = set()
def search_dir(dir_path: Path, depth: int = 0):
"""Recursively search directory."""
if str(dir_path) in visited_dirs:
return
visited_dirs.add(str(dir_path))
try:
for item in dir_path.iterdir():
if _should_exclude(item, exclude_patterns):
continue
if item.is_file():
if _match_glob(item, pattern):
matches.append(str(item))
if len(matches) >= max_results:
return True
elif item.is_dir():
# Handle ** pattern
if '**' in pattern:
search_dir(item, depth + 1)
elif depth < 20: # Limit depth for non-** patterns
search_dir(item, depth + 1)
except PermissionError:
pass
return False
search_dir(search_path)
return ToolResult(success=True, data={
"pattern": pattern,
"base_path": str(search_path),
"matches": matches,
"count": len(matches),
"truncated": len(matches) >= max_results
})
class GlobListTool(BaseTool):
"""List all files in directory with optional filtering."""
name = "glob_list"
description = "List files in directory with optional pattern filter"
input_schema = {
"type": "object",
"properties": {
"path": {"type": "string", "description": "Directory path"},
"pattern": {"type": "string", "description": "Optional pattern filter"},
"recursive": {"type": "boolean", "default": False, "description": "Recursive listing"},
"max_results": {"type": "number", "default": 500}
},
"required": ["path"]
}
async def execute(self, path: str, pattern: Optional[str] = None, recursive: bool = False, max_results: int = 500) -> ToolResult:
"""List directory contents."""
dir_path = Path(path)
if not dir_path.exists():
return ToolResult(success=False, error=f"Path not found: {path}")
matches = []
def search_dir(d: Path, depth: int = 0):
if len(matches) >= max_results:
return
try:
for item in d.iterdir():
if item.name.startswith('.'):
continue
if pattern:
if _match_glob(item, pattern):
matches.append(str(item))
else:
matches.append(str(item))
if item.is_dir() and recursive and depth < 10:
search_dir(item, depth + 1)
if len(matches) >= max_results:
return
except PermissionError:
pass
search_dir(dir_path)
return ToolResult(success=True, data={
"path": str(dir_path),
"files": matches,
"count": len(matches)
})
# Register tools
tool_registry.register(GlobTool())
tool_registry.register(GlobListTool())
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