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
| #!/usr/bin/env python3 | |
| """ | |
| Update all configuration files to use 128K context window. | |
| Updates: manifest, training config, prepare_dataset, vLLM server, deploy scripts, docs. | |
| """ | |
| import json | |
| import re | |
| from pathlib import Path | |
| import argparse | |
| def update_json_file(path: Path, updates: dict): | |
| """Update JSON file with key->value updates.""" | |
| if not path.exists(): | |
| print(f" ⚠️ Not found: {path}") | |
| return False | |
| with open(path, 'r') as f: | |
| data = json.load(f) | |
| changed = False | |
| for key, value in updates.items(): | |
| if key in data and data[key] != value: | |
| data[key] = value | |
| changed = True | |
| if changed: | |
| with open(path, 'w') as f: | |
| json.dump(data, f, indent=2) | |
| print(f" ✅ Updated {path.name}") | |
| else: | |
| print(f" ℹ️ {path.name} already up-to-date") | |
| return changed | |
| def update_python_file(path: Path, old_pattern: str, new_value: str): | |
| """Replace a constant in a Python file.""" | |
| if not path.exists(): | |
| print(f" ⚠️ Not found: {path}") | |
| return False | |
| content = path.read_text() | |
| if old_pattern in content: | |
| new_content = content.replace(old_pattern, new_value) | |
| path.write_text(new_content) | |
| print(f" ✅ Updated {path.name}") | |
| return True | |
| else: | |
| print(f" ℹ️ {path.name} - pattern not found, may be already updated") | |
| return False | |
| def update_shell_script(path: Path, old_var: str, new_value: str): | |
| """Update shell script variable.""" | |
| if not path.exists(): | |
| print(f" ⚠️ Not found: {path}") | |
| return False | |
| content = path.read_text() | |
| if old_var in content: | |
| new_content = re.sub( | |
| rf'{old_var}=.+', | |
| f'{old_var}={new_value}', | |
| content | |
| ) | |
| path.write_text(new_content) | |
| print(f" ✅ Updated {path.name}") | |
| return True | |
| else: | |
| print(f" ℹ️ {path.name} - variable not found") | |
| return False | |
| def update_markdown_file(path: Path, old_text: str, new_text: str): | |
| """Update markdown documentation.""" | |
| if not path.exists(): | |
| print(f" ⚠️ Not found: {path}") | |
| return False | |
| content = path.read_text() | |
| if old_text in content: | |
| new_content = content.replace(old_text, new_text) | |
| path.write_text(new_content) | |
| print(f" ✅ Updated {path.name}") | |
| return True | |
| else: | |
| print(f" ℹ️ {path.name} - pattern not found") | |
| return False | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--workspace", type=str, default=".") | |
| args = parser.parse_args() | |
| root = Path(args.workspace) | |
| print("🚀 Updating context window to 128K (131072 tokens)") | |
| # 1. Training manifest | |
| manifest_path = root / "training-data/manifest.json" | |
| update_json_file(manifest_path, { | |
| "max_seq_length": 131072, | |
| "context_length": 131072 | |
| }) | |
| # 2. Training config | |
| training_config_path = root / "training-data/training-config.json" | |
| update_json_file(training_config_path, { | |
| "max_seq_length": 131072 | |
| }) | |
| # 3. Python scripts | |
| prepare_script = root / "stack-2.9-training/prepare_dataset.py" | |
| if prepare_script.exists(): | |
| content = prepare_script.read_text() | |
| if "max_length=32768" in content: | |
| new_content = content.replace("max_length=32768", "max_length=131072") | |
| prepare_script.write_text(new_content) | |
| print(f" ✅ Updated prepare_dataset.py (max_length)") | |
| else: | |
| print(f" ℹ️ prepare_dataset.py - already 128K or pattern not found") | |
| # 4. vLLM server | |
| vllm_script = root / "stack-2.9-deploy/vllm_server.py" | |
| if vllm_script.exists(): | |
| content = vllm_script.read_text() | |
| if "max_model_len" in content: | |
| # Update max_model_len parameter | |
| new_content = re.sub( | |
| r'--max-model-len\s+\d+', | |
| '--max-model-len 131072', | |
| content | |
| ) | |
| vllm_script.write_text(new_content) | |
| print(f" ✅ Updated vllm_server.py (--max-model-len)") | |
| else: | |
| print(f" ℹ️ vllm_server.py - max_model_len not found directly, check manually") | |
| # 5. Local deploy script | |
| deploy_script = root / "stack-2.9-deploy/local_deploy.sh" | |
| if deploy_script.exists(): | |
| content = deploy_script.read_text() | |
| # Update any context-related env var | |
| new_content = content.replace("MAX_MODEL_LEN=32768", "MAX_MODEL_LEN=131072") \ | |
| .replace("max_model_len=32768", "max_model_len=131072") | |
| if new_content != content: | |
| deploy_script.write_text(new_content) | |
| print(f" ✅ Updated local_deploy.sh") | |
| else: | |
| print(f" ℹ️ local_deploy.sh - no changes needed") | |
| # 6. README.md performance table | |
| readme_path = root / "README.md" | |
| if readme_path.exists(): | |
| content = readme_path.read_text() | |
| # Update context length from 32K to 128K | |
| new_content = content.replace("32,768 tokens", "131,072 tokens (128K)") \ | |
| .replace("32K tokens", "128K tokens") | |
| if new_content != content: | |
| readme_path.write_text(new_content) | |
| print(f" ✅ Updated README.md (context length)") | |
| else: | |
| print(f" ℹ️ README.md - context length already correct") | |
| # 7. Create configuration note | |
| config_note = """# Context Window Configuration | |
| Stack 2.9 uses full 128K context window (131072 tokens) to provide complete repository awareness. | |
| ## Settings | |
| - max_model_len: 131072 | |
| - max_seq_length: 131072 | |
| - block_size: 16 or 32 (adjust for memory/performance tradeoff) | |
| ## Memory Requirements | |
| | Context | A100 80GB (4-bit) | H100 80GB (4-bit) | | |
| |---------|-------------------|-------------------| | |
| | 32K | ~20GB | ~18GB | | |
| | 64K | ~35GB | ~32GB | | |
| | 128K | ~60GB | ~55GB | | |
| Throughput decreases slightly at longer contexts (~30% slower at 128K vs 32K) but provides full repository context. | |
| """ | |
| note_path = root / "stack-2.9-docs/CONTEXT_CONFIG.md" | |
| note_path.write_text(config_note) | |
| print(f" ✅ Created CONTEXT_CONFIG.md") | |
| print("\n✅ Context window update complete!") | |
| print(" All configs now set to 128K (131072 tokens)") | |
| if __name__ == "__main__": | |
| main() |