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: 6,502 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 | #!/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() |