This repo contains an in-house tuned LLaMA-7b based on the Stanford Alpaca dataset, for only research use.
Quantitative evaluation on machine translation and qualitative comparison on general abilities can be found at alpaca-mt.
| Translation Performance of LLMs on Flores Subsets. | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Direction | De-En | En-De | Zh-En | En-Zh | |||||||
| Metric | BLEU | COMET | BLEU | COMET | BLEU | COMET | BLEU | COMET | |||
| 45.04 | 0.8879 | 41.16 | 0.8861 | 31.66 | 0.8771 | 43.58 | 0.8842 | ||||
| DeepL | 49.23 | 0.8970 | 41.46 | 0.8903 | 31.22 | 0.8739 | 44.31 | 0.8811 | |||
| ChatGPT | 43.71 | 0.8910 | 38.87 | 0.8814 | 24.73 | 0.8581 | 38.27 | 0.8699 | |||
| GPT-4 | 46.00 | 0.8931 | 45.73 | 0.8928 | 28.50 | 0.8742 | 42.50 | 0.8840 | |||
| LLaMA-7b | 6.96 | 0.6548 | 3.64 | 0.5084 | 8.95 | 0.6340 | 0.10 | 0.4899 | |||
| Alpaca-7b | 36.00 | 0.8737 | 20.09 | 0.8003 | 14.37 | 0.8069 | 10.06 | 0.5604 | |||
- Downloads last month
- 1,275
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.