File size: 2,719 Bytes
067b467 1b120c3 067b467 0014a37 067b467 0014a37 067b467 0014a37 067b467 |
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 78 79 80 81 82 83 84 85 86 87 |
---
library_name: transformers
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: prm_version3_full_hf
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. -->
# prm_version3_full_hf
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 prm_conversations_prm_version3_math+webinstructsub-mcq+webinstructsub-oe+apps+gsm_mix_ref_hf dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1166
## 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: 5e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.1961 | 0.0461 | 500 | 0.2069 |
| 0.192 | 0.0921 | 1000 | 0.1930 |
| 0.1963 | 0.1382 | 1500 | 0.1833 |
| 0.1701 | 0.1843 | 2000 | 0.1748 |
| 0.1647 | 0.2303 | 2500 | 0.1687 |
| 0.1507 | 0.2764 | 3000 | 0.1630 |
| 0.1421 | 0.3225 | 3500 | 0.1579 |
| 0.1403 | 0.3685 | 4000 | 0.1528 |
| 0.1557 | 0.4146 | 4500 | 0.1485 |
| 0.1536 | 0.4607 | 5000 | 0.1441 |
| 0.1344 | 0.5067 | 5500 | 0.1399 |
| 0.1195 | 0.5528 | 6000 | 0.1355 |
| 0.1209 | 0.5989 | 6500 | 0.1316 |
| 0.137 | 0.6450 | 7000 | 0.1284 |
| 0.117 | 0.6910 | 7500 | 0.1253 |
| 0.116 | 0.7371 | 8000 | 0.1228 |
| 0.1259 | 0.7832 | 8500 | 0.1206 |
| 0.1147 | 0.8292 | 9000 | 0.1187 |
| 0.1175 | 0.8753 | 9500 | 0.1175 |
| 0.1117 | 0.9214 | 10000 | 0.1168 |
| 0.1133 | 0.9674 | 10500 | 0.1166 |
### Framework versions
- Transformers 4.45.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|