FactAlign-LLaMA-3-8B
This model is aligned with our FactAlign framework for improved long-form factuality, from meta-llama/Meta-Llama-3-8B-Instruct.
For more information, please refer to our paper: FactAlign: Long-form Factuality Alignment of Large Language Models.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the trl-lib/kto-mix-14k and the chaoweihuang/lf-response-llama3-f1_100_0.8-fg0.5 datasets. It achieves the following results on the evaluation set:
- Loss: 0.4110
- Rewards/chosen: 1.7360
- Logps/chosen: -336.0412
- Rewards/rejected: -2.2628
- Logps/rejected: -406.1173
- Rewards/margins: 3.9987
- Kl: 0.0141
- Fg Rewards/chosen Sum: -1.5560
- Fg Logps/policy Chosen: -6.7332
- Fg Logps/reference Chosen: -6.0419
- Count/fg Chosen: 30.1832
- Fg Rewards/rejected Sum: -0.9033
- Fg Logps/policy Rejected: -8.6269
- Fg Logps/reference Rejected: -7.5807
- Count/fg Rejected: 6.9239
- Fg Logps/policy Kl: -14.7946
- Fg Logps/reference Kl: -11.4736
- Fg Kl: nan
- Fg Loss: 0.7625
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Logps/chosen | Rewards/rejected | Logps/rejected | Rewards/margins | Kl | Fg Rewards/chosen Sum | Fg Logps/policy Chosen | Fg Logps/reference Chosen | Count/fg Chosen | Fg Rewards/rejected Sum | Fg Logps/policy Rejected | Fg Logps/reference Rejected | Count/fg Rejected | Fg Logps/policy Kl | Fg Logps/reference Kl | Fg Kl | Fg Loss |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4478 | 0.4103 | 400 | 0.4325 | 1.3169 | -340.2313 | -1.7364 | -400.8539 | 3.0534 | 0.0280 | -1.3939 | -6.6287 | -6.0419 | 30.1832 | -0.6768 | -8.3632 | -7.5807 | 6.9239 | -13.6783 | -11.4736 | nan | 0.7654 |
| 0.4043 | 0.8205 | 800 | 0.4110 | 1.7360 | -336.0412 | -2.2628 | -406.1173 | 3.9987 | 0.0141 | -1.5560 | -6.7332 | -6.0419 | 30.1832 | -0.9033 | -8.6269 | -7.5807 | 6.9239 | -14.7946 | -11.4736 | nan | 0.7625 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1