File size: 2,047 Bytes
93543e4 7c3e09e 93543e4 7c3e09e 93543e4 7c3e09e 93543e4 7c3e09e 93543e4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
---
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
datasets:
- hansh/hansken_hql_cot
library_name: peft
license: llama3.1
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
model-index:
- name: hansken_human_hql_v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hansken_human_hql_v3
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co./meta-llama/Meta-Llama-3.1-8B-Instruct) on the hansh/hansken_hql_cot dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5017
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6267 | 1.0 | 469 | 0.6078 |
| 0.5094 | 2.0 | 938 | 0.5335 |
| 0.513 | 3.0 | 1407 | 0.5142 |
| 0.4306 | 4.0 | 1876 | 0.5044 |
| 0.4128 | 5.0 | 2345 | 0.5017 |
| 0.3924 | 6.0 | 2814 | 0.5093 |
| 0.3684 | 7.0 | 3283 | 0.5168 |
| 0.3403 | 8.0 | 3752 | 0.5338 |
| 0.311 | 9.0 | 4221 | 0.5566 |
| 0.2853 | 10.0 | 4690 | 0.5920 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.43.1
- Pytorch 2.1.2+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |