model-index:
- name: Junrulu/Reproduced-tulu2-dpo-13b
results: []
datasets:
- HuggingFaceH4/ultrafeedback_binarized
- Junrulu/Reproduced-tulu2-test-sets
language:
- en
base_model: allenai/tulu-2-13b
Model Card for Reproduced Tulu2 DPO 13B
This repository provides a reproduction version of Tulu2-DPO-13B finetuned upon Tulu2-13B and Ultrafeedback. Therefore, we obey all licenses mentioned in Tulu2's work. Check our codes for more details: https://github.com/LuJunru/LLM_Finetune/tree/DPO, which is built with TRL.
Performance
| Model | Size | Alignment | MT-Bench (score) | AlpacaEval 2.0 (win rate %) |
|---|---|---|---|---|
| Tulu-v2-13b 🐪 | 13B | SFT | 5.79 | 2.61 |
| Tulu-v2-dpo-13b 🐪 | 13B | DPO | 6.06 | 6.96 |
| Reproduced-tulu2-dpo-13b | 13B | DPO | 6.27 | 6.71 |
Input Format
The model is trained to use the following format (note the newlines):
<|user|>
Your message here!
<|assistant|>
For best results, format all inputs in this manner. Make sure to include a newline after <|assistant|>, this can affect generation quality quite a bit. Note: if fine-tuning with this chat template, ensure to evaluate and test with the chat template. Otherwise, fine-tining without the template if you choose to not use template during testing. Any mismatch of the chatting template between training and testing phases can obviously dampen the final performance.
Training hyperparameters
The following hyperparameters were used during DPO training:
- DPO beta: 0.1
- learning_rate: 1e-6 * sqrt(Num of Nodes)
- total_train_batch_size: 128 * Num of Nodes
- optimizer: AdamW with beta1 0.9, beta2 0.999 and epsilon 1e-8
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- Weight Decay: 0.0
- num_epochs: 3.0
- Specifically add above input format over training samples