On Oxen.ai, fine-tuning is as easy as uploading your dataset, selecting a model, and kicking off a job. The dataset can be any of the supported tabular file formats such as csv, tsv, json, or parquet. Learn more about fine-tuning in our developer docs.
Existing fine tunes
10ID | Base Model | Model Name | Training Dataset | Status | Created By | Created At |
---|---|---|---|---|---|---|
meta-llama/Llama-3.2-1B-Instruct | mathi-senior-rose-platypus | 48608fb3train.jsonl | Deployed | mathi | 2 weeks ago | |
meta-llama/Llama-3.2-1B-Instruct | mathi-wild-azure-haddock | f7550b67train.jsonl | Deployed | mathi | 2 weeks ago | |
meta-llama/Llama-3.2-1B-Instruct | f7550b67train.jsonl | Failed | mathi | 2 weeks ago | ||
meta-llama/Llama-3.2-1B-Instruct | f7550b67train.jsonl | Success | mathi | 3 weeks ago | ||
meta-llama/Llama-3.2-1B-Instruct | f7550b67train.jsonl | Success | mathi | 3 weeks ago | ||
meta-llama/Llama-3.2-1B-Instruct | f7550b67train.jsonl | Success | mathi | 3 weeks ago | ||
meta-llama/Llama-3.2-3B-Instruct | mathi-electoral-tan-locust | f7550b67train.jsonl | Deployed | mathi | 4 weeks ago | |
meta-llama/Llama-3.2-3B-Instruct | f7550b67train.jsonl | Success | mathi | 4 weeks ago | ||
meta-llama/Llama-3.2-1B-Instruct | f7550b67train.jsonl | Success | mathi | 4 weeks ago | ||
meta-llama/Llama-3.2-3B-Instruct | f7550b67train.jsonl | Success | mathi | 4 weeks ago |