Instructions to use andstor/Qwen-Qwen2.5-Coder-7B-unit-test-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use andstor/Qwen-Qwen2.5-Coder-7B-unit-test-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B") model = PeftModel.from_pretrained(base_model, "andstor/Qwen-Qwen2.5-Coder-7B-unit-test-lora") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from andstor/Qwen-Qwen2.5-Coder-7B-unit-test-lora: direct link, hf CLI and curl.
- Browser
- Download file 251 Bytes
-
https://huggingface.co/andstor/Qwen-Qwen2.5-Coder-7B-unit-test-lora/resolve/main/train_results.json
- Command line
-
hf download hf://andstor/Qwen-Qwen2.5-Coder-7B-unit-test-lora/train_results.json
-
curl -L -o train_results.json https://huggingface.co/andstor/Qwen-Qwen2.5-Coder-7B-unit-test-lora/resolve/main/train_results.json
251 Bytes
| { | |
| "epoch": 2.9983597594313833, | |
| "total_flos": 4.3228174920083046e+17, | |
| "train_loss": 0.7109334499926396, | |
| "train_runtime": 1998.4313, | |
| "train_samples": 7316, | |
| "train_samples_per_second": 10.983, | |
| "train_steps_per_second": 0.686 | |
| } |