Instructions to use andstor/Qwen-Qwen2.5-Coder-7B-unit-test-prompt-tuning 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-prompt-tuning 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-prompt-tuning") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from andstor/Qwen-Qwen2.5-Coder-7B-unit-test-prompt-tuning: direct link, hf CLI and curl.
- Browser
- Download file 250 Bytes
-
https://huggingface.co/andstor/Qwen-Qwen2.5-Coder-7B-unit-test-prompt-tuning/resolve/main/train_results.json
- Command line
-
hf download hf://andstor/Qwen-Qwen2.5-Coder-7B-unit-test-prompt-tuning/train_results.json
-
curl -L -o train_results.json https://huggingface.co/andstor/Qwen-Qwen2.5-Coder-7B-unit-test-prompt-tuning/resolve/main/train_results.json
250 Bytes
| { | |
| "epoch": 2.999179655455291, | |
| "total_flos": 4.3134948379459584e+17, | |
| "train_loss": 0.7785058324133541, | |
| "train_runtime": 1561.6761, | |
| "train_samples": 7313, | |
| "train_samples_per_second": 14.048, | |
| "train_steps_per_second": 0.878 | |
| } |