Instructions to use xxccho/margin_reg_baseline_code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use xxccho/margin_reg_baseline_code with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("meta-llama/Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "xxccho/margin_reg_baseline_code") - Transformers
How to use xxccho/margin_reg_baseline_code with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xxccho/margin_reg_baseline_code", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| epoch,step,domain,eval_loss,eval_accuracy,margin_mean | |
| 1.0,246,general,0.754059910774231,0.5846994535519126,0.49009084701538086 | |
| 1.0,246,code,0.0,0.5915032679738562,0.7128996253013611 | |
| 1.0,246,stem,0.0,0.574398249452954,0.3838408887386322 | |
| 2.0,492,general,0.6887519359588623,0.6327868852459017,0.7847661375999451 | |
| 2.0,492,code,0.0,0.673202614379085,1.3276170492172241 | |
| 2.0,492,stem,0.0,0.6225382932166302,0.7555620670318604 | |
| 3.0,738,general,0.6758893728256226,0.6327868852459017,0.8038123250007629 | |
| 3.0,738,code,0.0,0.6895424836601307,1.5014357566833496 | |
| 3.0,738,stem,0.0,0.6105032822757112,0.8135308027267456 | |
| 4.0,984,general,0.6872960329055786,0.6415300546448087,0.997459352016449 | |
| 4.0,984,code,0.0,0.6938997821350763,1.9135780334472656 | |
| 4.0,984,stem,0.0,0.6137855579868708,1.0132267475128174 | |
| 5.0,1230,general,0.7001935839653015,0.6426229508196721,1.190500020980835 | |
| 5.0,1230,code,0.0,0.710239651416122,2.26448917388916 | |
| 5.0,1230,stem,0.0,0.6148796498905909,1.1789042949676514 | |
| 6.0,1476,general,0.7351013422012329,0.6480874316939891,1.3944945335388184 | |
| 6.0,1476,code,0.0,0.7331154684095861,2.7008097171783447 | |
| 6.0,1476,stem,0.0,0.6050328227571116,1.398270845413208 | |
| 7.0,1722,general,0.7735296487808228,0.644808743169399,1.5480133295059204 | |
| 7.0,1722,code,0.0,0.7363834422657952,3.104238510131836 | |
| 7.0,1722,stem,0.0,0.5995623632385121,1.563852310180664 | |
| 8.0,1968,general,0.8415325880050659,0.6622950819672131,1.871593952178955 | |
| 8.0,1968,code,0.0,0.7418300653594772,3.681284189224243 | |
| 8.0,1968,stem,0.0,0.6094091903719913,1.9072976112365723 | |
| 9.0,2214,general,0.8703850507736206,0.6513661202185792,1.9912307262420654 | |
| 9.0,2214,code,0.0,0.7429193899782135,3.9107680320739746 | |
| 9.0,2214,stem,0.0,0.6148796498905909,2.022435188293457 | |
| 10.0,2460,general,0.8984853029251099,0.6546448087431694,2.0600435733795166 | |
| 10.0,2460,code,0.0,0.7429193899782135,4.078938961029053 | |
| 10.0,2460,stem,0.0,0.6094091903719913,2.092808723449707 | |