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: 7,312 Bytes
6379283 | 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 | #!/usr/bin/env python3
"""
Extract code-comment pairs from the src/ directory.
Pairs: function/class code + its documentation comment.
"""
import os
import re
import json
from pathlib import Path
from typing import List, Dict, Any
import argparse
def extract_jsdoc_comments(content: str) -> List[Dict[str, Any]]:
"""Extract JSDoc comments and associated code from JS/TS files."""
pairs = []
# Pattern to match JSDoc comment block followed by code
# Matches: /** ... */ followed by function/class/interface
pattern = re.compile(
r'/\*\*\s*(.*?)\s*\*/\s*' # JSDoc comment
r'(export\s+)?(async\s+)?(function|const|let|var|class|interface|type)\s+(\w+)',
re.DOTALL
)
for match in pattern.finditer(content):
comment_lines = match.group(1).strip().split('\n')
# Clean up comment markers
comment = []
for line in comment_lines:
line = line.strip()
if line.startswith('* '):
line = line[2:]
elif line.startswith('*'):
line = line[1:]
comment.append(line.strip())
comment_text = ' '.join(comment).strip()
code_start = match.end()
# Extract the function signature or class definition (up to opening brace or newline)
code_lines = []
lines = content[code_start:].split('\n')
for line in lines[:5]: # Take first few lines
code_lines.append(line)
if line.strip().endswith('{') or line.strip().endswith('>'):
break
code = '\n'.join(code_lines).strip()
if comment_text and code and len(code.split('\n')) >= 2:
pairs.append({
"code": code,
"comment": comment_text,
"type": match.group(3), # function/class/interface
"name": match.group(4)
})
return pairs
def extract_python_docstrings(content: str) -> List[Dict[str, Any]]:
"""Extract Python docstrings and associated code."""
pairs = []
# Pattern for triple-quoted docstring before function/class
pattern = re.compile(
r'''(?P<quote>''' + r'"""' + r'''|\'\'\')\s*(?P<doc>.*?)(?P=quote)\s*'''
r'(?:@\w+\s+)*def\s+(\w+)|class\s+(\w+)',
re.DOTALL
)
for match in pattern.finditer(content):
doc = match.group('doc').strip()
func_name = match.group(3) or match.group(4)
if func_name:
# Get the signature line
signature = content[match.end():].split('\n')[0].strip()
code = f"def {func_name}{signature}" if 'def' in signature else f"class {func_name}{signature}"
pairs.append({
"code": code,
"comment": doc,
"type": "function" if 'def' in signature else "class",
"name": func_name
})
return pairs
def extract_inline_comments(content: str, file_ext: str) -> List[Dict[str, Any]]:
"""Extract code block with preceding inline comment."""
pairs = []
lines = content.split('\n')
i = 0
while i < len(lines):
line = lines[i].rstrip()
# Check for // comment or # comment
if line.strip().startswith('//') or line.strip().startswith('#'):
comment = line.strip()[2:].strip()
# Look at next few lines for code
code_lines = []
j = i + 1
while j < len(lines) and len(code_lines) < 5:
next_line = lines[j].rstrip()
if next_line.strip() and not next_line.strip().startswith('//') and not next_line.strip().startswith('#'):
code_lines.append(next_line)
elif next_line.strip().startswith(('//', '#')):
break # Another comment block
j += 1
if comment and code_lines:
code = '\n'.join(code_lines)
# Only keep if comment is meaningful (>5 words or contains specific keywords)
if len(comment.split()) > 3 or any(kw in comment.lower() for kw in ['function', 'return', 'parameter', 'args', 'handle', 'process']):
pairs.append({
"code": code,
"comment": comment,
"type": "inline",
"name": None
})
i = j # Skip processed lines
else:
i += 1
else:
i += 1
return pairs
def process_file(file_path: Path) -> List[Dict[str, Any]]:
"""Process a single file and extract code-comment pairs."""
try:
with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
content = f.read()
except Exception as e:
print(f"❌ Error reading {file_path}: {e}")
return []
pairs = []
# Extract by file type
if file_path.suffix in ['.js', '.ts', '.jsx', '.tsx']:
pairs.extend(extract_jsdoc_comments(content))
elif file_path.suffix == '.py':
pairs.extend(extract_python_docstrings(content))
# Inline comments for all types
pairs.extend(extract_inline_comments(content, file_path.suffix))
return pairs
def walk_source_files(src_dir: Path) -> List[Path]:
"""Walk src/ directory and return all relevant source files."""
extensions = ['.ts', '.tsx', '.js', '.jsx', '.py']
files = []
for ext in extensions:
files.extend(src_dir.rglob(f'*{ext}'))
return files
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--src-dir", type=str, default="src")
parser.add_argument("--output", type=str, default="training-data/code-pairs/extended_pairs.json")
parser.add_argument("--limit", type=int, default=10000, help="Maximum pairs to extract")
args = parser.parse_args()
src_dir = Path(args.src_dir)
output_path = Path(args.output)
if not src_dir.exists():
print(f"❌ Source directory not found: {src_dir}")
return
print(f"🔍 Scanning {src_dir} for source files...")
files = walk_source_files(src_dir)
print(f" Found {len(files)} source files")
all_pairs = []
for file_path in files:
pairs = process_file(file_path)
if pairs:
all_pairs.extend(pairs)
print(f" {file_path.name}: {len(pairs)} pairs", end='\r')
if len(all_pairs) >= args.limit:
break
print(f"\n✨ Extracted {len(all_pairs)} code-comment pairs")
# Deduplicate (by comment+code hash)
seen = set()
unique_pairs = []
for pair in all_pairs:
key = (pair['comment'][:100], pair['code'][:100])
if key not in seen:
seen.add(key)
unique_pairs.append(pair)
print(f" After deduplication: {len(unique_pairs)} unique pairs")
# Save
output_path.parent.mkdir(parents=True, exist_ok=True)
with open(output_path, 'w') as f:
json.dump(unique_pairs, f, indent=2)
print(f"✅ Saved to: {output_path}")
# Stats
types = {}
for pair in unique_pairs:
t = pair.get('type', 'unknown')
types[t] = types.get(t, 0) + 1
print("\n📊 By type:")
for t, cnt in types.items():
print(f" {t}: {cnt}")
if __name__ == "__main__":
main() |