metadata
base_model: meta-llama/Llama-2-13b-chat-hf
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: llama-2-13b-reward-oasst1
results: []
library_name: peft
llama-2-13b-reward-oasst1
This model is a fine-tuned version of meta-llama/Llama-2-13b-chat-hf on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4810
- Accuracy: 0.7869
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
The following bitsandbytes quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5602 | 0.08 | 250 | 0.5436 | 0.7388 |
| 0.6166 | 0.17 | 500 | 0.5340 | 0.7468 |
| 0.6545 | 0.25 | 750 | 0.4899 | 0.7644 |
| 0.5635 | 0.33 | 1000 | 0.4877 | 0.7532 |
| 0.5933 | 0.42 | 1250 | 0.4930 | 0.7660 |
| 0.5758 | 0.5 | 1500 | 0.4851 | 0.7740 |
| 0.5212 | 0.58 | 1750 | 0.5021 | 0.7788 |
| 0.5251 | 0.67 | 2000 | 0.4893 | 0.7804 |
| 0.5145 | 0.75 | 2250 | 0.4924 | 0.7853 |
| 0.5085 | 0.83 | 2500 | 0.4934 | 0.7853 |
| 0.617 | 0.92 | 2750 | 0.4803 | 0.7821 |
| 0.5525 | 1.0 | 3000 | 0.4810 | 0.7869 |
Framework versions
- PEFT 0.5.0.dev0
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3