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
11| ID | Base Model | Model Name | Display Name | Training Dataset | Training Status | Deployment Status | Created By | Created At | |
|---|---|---|---|---|---|---|---|---|---|
| meta-llama/Llama-3.2-1B-Instruct | 48608fb3train.jsonl | Success | Not Deployed | mathi | 4 months ago | ||||
| meta-llama/Llama-3.2-1B-Instruct | mathi-senior-rose-platypus | 48608fb3train.jsonl | Success | Sleeping | mathi | 5 months ago | |||
| meta-llama/Llama-3.2-1B-Instruct | mathi-wild-azure-haddock | f7550b67train.jsonl | Success | Sleeping | mathi | 5 months ago | |||
| meta-llama/Llama-3.2-1B-Instruct | f7550b67train.jsonl | Failed | Not Deployed | mathi | 5 months ago | ||||
| meta-llama/Llama-3.2-1B-Instruct | f7550b67train.jsonl | Success | Not Deployed | mathi | 5 months ago | ||||
| meta-llama/Llama-3.2-1B-Instruct | f7550b67train.jsonl | Success | Not Deployed | mathi | 5 months ago | ||||
| meta-llama/Llama-3.2-1B-Instruct | f7550b67train.jsonl | Success | Not Deployed | mathi | 5 months ago | ||||
| meta-llama/Llama-3.2-3B-Instruct | mathi-electoral-tan-locust | f7550b67train.jsonl | Success | Inactive | mathi | 5 months ago | |||
| meta-llama/Llama-3.2-3B-Instruct | f7550b67train.jsonl | Success | Not Deployed | mathi | 5 months ago | ||||
| meta-llama/Llama-3.2-1B-Instruct | f7550b67train.jsonl | Success | Not Deployed | mathi | 5 months ago | ||||
| meta-llama/Llama-3.2-3B-Instruct | f7550b67train.jsonl | Success | Not Deployed | mathi | 5 months ago |