---
library_name: transformers
license: apache-2.0
base_model: amd/AMD-Llama-135m
tags:
- generated_from_trainer
model-index:
- name: amdchess-v5
  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. -->

# amdchess-v5

This model is a fine-tuned version of [amd/AMD-Llama-135m](https://huggingface.co/amd/AMD-Llama-135m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7610

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use grokadamw with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 0.25

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.9045        | 0.0030 | 5    | 2.7322          |
| 1.5833        | 0.0059 | 10   | 1.7005          |
| 1.5115        | 0.0089 | 15   | 1.3183          |
| 1.0591        | 0.0118 | 20   | 1.3213          |
| 1.1079        | 0.0148 | 25   | 1.1174          |
| 1.1004        | 0.0177 | 30   | 1.1248          |
| 1.0783        | 0.0207 | 35   | 1.0751          |
| 1.0209        | 0.0236 | 40   | 1.0297          |
| 1.0955        | 0.0266 | 45   | 1.0330          |
| 1.1106        | 0.0295 | 50   | 1.0172          |
| 1.0855        | 0.0325 | 55   | 0.9780          |
| 0.979         | 0.0354 | 60   | 0.9635          |
| 0.8885        | 0.0384 | 65   | 0.9590          |
| 0.9195        | 0.0413 | 70   | 0.9452          |
| 0.9518        | 0.0443 | 75   | 0.9325          |
| 0.9609        | 0.0472 | 80   | 0.9332          |
| 0.9327        | 0.0502 | 85   | 0.9229          |
| 0.9621        | 0.0531 | 90   | 0.9157          |
| 0.9956        | 0.0561 | 95   | 0.9094          |
| 0.8193        | 0.0590 | 100  | 0.8958          |
| 0.9361        | 0.0620 | 105  | 0.8915          |
| 0.9039        | 0.0649 | 110  | 0.8882          |
| 0.8757        | 0.0679 | 115  | 0.8813          |
| 0.8875        | 0.0708 | 120  | 0.8776          |
| 0.8989        | 0.0738 | 125  | 0.8805          |
| 0.9478        | 0.0767 | 130  | 0.8706          |
| 0.9132        | 0.0797 | 135  | 0.8645          |
| 0.8755        | 0.0826 | 140  | 0.8607          |
| 0.9304        | 0.0856 | 145  | 0.8559          |
| 0.8711        | 0.0885 | 150  | 0.8466          |
| 0.8511        | 0.0915 | 155  | 0.8480          |
| 0.8768        | 0.0945 | 160  | 0.8410          |
| 0.6914        | 0.0974 | 165  | 0.8407          |
| 0.8625        | 0.1004 | 170  | 0.8342          |
| 0.8219        | 0.1033 | 175  | 0.8370          |
| 0.9106        | 0.1063 | 180  | 0.8296          |
| 0.8512        | 0.1092 | 185  | 0.8253          |
| 0.8286        | 0.1122 | 190  | 0.8251          |
| 0.9075        | 0.1151 | 195  | 0.8214          |
| 0.8733        | 0.1181 | 200  | 0.8199          |
| 0.7881        | 0.1210 | 205  | 0.8164          |
| 0.9131        | 0.1240 | 210  | 0.8150          |
| 0.8421        | 0.1269 | 215  | 0.8104          |
| 0.8589        | 0.1299 | 220  | 0.8083          |
| 0.7674        | 0.1328 | 225  | 0.8065          |
| 0.8566        | 0.1358 | 230  | 0.8065          |
| 0.8657        | 0.1387 | 235  | 0.8019          |
| 0.7534        | 0.1417 | 240  | 0.7992          |
| 0.7988        | 0.1446 | 245  | 0.7970          |
| 0.8197        | 0.1476 | 250  | 0.7937          |
| 0.8175        | 0.1505 | 255  | 0.7931          |
| 0.8831        | 0.1535 | 260  | 0.7915          |
| 0.8714        | 0.1564 | 265  | 0.7882          |
| 0.8097        | 0.1594 | 270  | 0.7864          |
| 0.7864        | 0.1623 | 275  | 0.7849          |
| 0.7521        | 0.1653 | 280  | 0.7845          |
| 0.8208        | 0.1682 | 285  | 0.7820          |
| 0.7658        | 0.1712 | 290  | 0.7802          |
| 0.8623        | 0.1741 | 295  | 0.7782          |
| 0.8526        | 0.1771 | 300  | 0.7765          |
| 0.8304        | 0.1800 | 305  | 0.7749          |
| 0.823         | 0.1830 | 310  | 0.7737          |
| 0.762         | 0.1860 | 315  | 0.7726          |
| 0.7545        | 0.1889 | 320  | 0.7715          |
| 0.7818        | 0.1919 | 325  | 0.7699          |
| 0.7601        | 0.1948 | 330  | 0.7699          |
| 0.7414        | 0.1978 | 335  | 0.7689          |
| 0.8397        | 0.2007 | 340  | 0.7682          |
| 0.8282        | 0.2037 | 345  | 0.7668          |
| 0.7676        | 0.2066 | 350  | 0.7655          |
| 0.7768        | 0.2096 | 355  | 0.7644          |
| 0.7249        | 0.2125 | 360  | 0.7639          |
| 0.7633        | 0.2155 | 365  | 0.7635          |
| 0.721         | 0.2184 | 370  | 0.7632          |
| 0.798         | 0.2214 | 375  | 0.7624          |
| 0.7601        | 0.2243 | 380  | 0.7620          |
| 0.8439        | 0.2273 | 385  | 0.7618          |
| 0.777         | 0.2302 | 390  | 0.7616          |
| 0.6739        | 0.2332 | 395  | 0.7614          |
| 0.802         | 0.2361 | 400  | 0.7612          |
| 0.7868        | 0.2391 | 405  | 0.7611          |
| 0.6621        | 0.2420 | 410  | 0.7610          |
| 0.7723        | 0.2450 | 415  | 0.7610          |
| 0.8052        | 0.2479 | 420  | 0.7610          |


### Framework versions

- Transformers 4.46.0
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.20.1