Text Generation
PEFT
Safetensors
English
lora
qwen2.5
civil-nuclear
reactor-physics
reactor-kinetics
synthetic-data
vers3dynamics
conversational
Instructions to use ciaochris/Vers3Dynamics-Civil-Reactor-Expert-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ciaochris/Vers3Dynamics-Civil-Reactor-Expert-3B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B-Instruct") model = PeftModel.from_pretrained(base_model, "ciaochris/Vers3Dynamics-Civil-Reactor-Expert-3B") - Notebooks
- Google Colab
- Kaggle
| import importlib | |
| import unittest | |
| class ExampleImportTests(unittest.TestCase): | |
| def test_example_scripts_import_without_ml_dependencies(self): | |
| infer = importlib.import_module("examples.infer_transformers") | |
| endpoint = importlib.import_module("examples.call_local_endpoint") | |
| server = importlib.import_module("examples.serve_local_endpoint") | |
| self.assertEqual(infer.DEFAULT_BASE_MODEL, "Qwen/Qwen2.5-3B-Instruct") | |
| self.assertEqual(server.DEFAULT_ADAPTER_PATH, ".") | |
| payload = endpoint.build_payload("Explain delayed neutrons.", model="local", max_tokens=128) | |
| self.assertEqual(payload["messages"][1]["role"], "user") | |
| self.assertIn("civil-nuclear", payload["messages"][0]["content"]) | |
| if __name__ == "__main__": | |
| unittest.main() | |