---
library_name: transformers
license: apache-2.0
base_model: amd/AMD-Llama-135m
tags:
- generated_from_trainer
model-index:
- name: amdchess-v4
  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-v4

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: 0.7971

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 9.9629        | 0.0030 | 5    | 5.6096          |
| 3.7446        | 0.0059 | 10   | 3.3680          |
| 2.524         | 0.0089 | 15   | 2.3223          |
| 1.9286        | 0.0118 | 20   | 1.7446          |
| 1.5475        | 0.0148 | 25   | 2.0681          |
| 1.2838        | 0.0177 | 30   | 1.4096          |
| 1.3152        | 0.0207 | 35   | 1.2730          |
| 1.2488        | 0.0236 | 40   | 1.2203          |
| 1.088         | 0.0266 | 45   | 1.1461          |
| 1.0479        | 0.0295 | 50   | 1.1139          |
| 1.0758        | 0.0325 | 55   | 1.0844          |
| 1.1275        | 0.0354 | 60   | 1.0443          |
| 1.1378        | 0.0384 | 65   | 1.0260          |
| 1.0147        | 0.0413 | 70   | 0.9939          |
| 0.993         | 0.0443 | 75   | 1.0074          |
| 1.0132        | 0.0472 | 80   | 0.9866          |
| 0.9155        | 0.0502 | 85   | 0.9697          |
| 0.9656        | 0.0531 | 90   | 0.9757          |
| 1.0402        | 0.0561 | 95   | 0.9633          |
| 0.9759        | 0.0590 | 100  | 0.9528          |
| 0.9505        | 0.0620 | 105  | 0.9501          |
| 1.0114        | 0.0649 | 110  | 0.9405          |
| 1.0182        | 0.0679 | 115  | 0.9212          |
| 0.9396        | 0.0708 | 120  | 0.9284          |
| 0.902         | 0.0738 | 125  | 0.9262          |
| 0.9533        | 0.0767 | 130  | 0.9121          |
| 0.8755        | 0.0797 | 135  | 0.9160          |
| 0.9349        | 0.0826 | 140  | 0.9083          |
| 0.9585        | 0.0856 | 145  | 0.8993          |
| 0.8349        | 0.0885 | 150  | 0.9000          |
| 0.9541        | 0.0915 | 155  | 0.8887          |
| 0.9108        | 0.0945 | 160  | 0.8837          |
| 0.9196        | 0.0974 | 165  | 0.8806          |
| 0.9094        | 0.1004 | 170  | 0.8776          |
| 0.8514        | 0.1033 | 175  | 0.8759          |
| 0.7515        | 0.1063 | 180  | 0.8684          |
| 0.8031        | 0.1092 | 185  | 0.8676          |
| 0.8639        | 0.1122 | 190  | 0.8661          |
| 0.8002        | 0.1151 | 195  | 0.8556          |
| 0.7812        | 0.1181 | 200  | 0.8574          |
| 0.9163        | 0.1210 | 205  | 0.8582          |
| 0.8824        | 0.1240 | 210  | 0.8515          |
| 0.8759        | 0.1269 | 215  | 0.8502          |
| 0.8384        | 0.1299 | 220  | 0.8467          |
| 0.8436        | 0.1328 | 225  | 0.8427          |
| 0.8329        | 0.1358 | 230  | 0.8398          |
| 0.87          | 0.1387 | 235  | 0.8393          |
| 0.8405        | 0.1417 | 240  | 0.8356          |
| 0.8634        | 0.1446 | 245  | 0.8339          |
| 0.8298        | 0.1476 | 250  | 0.8315          |
| 0.7582        | 0.1505 | 255  | 0.8278          |
| 0.7912        | 0.1535 | 260  | 0.8257          |
| 0.8878        | 0.1564 | 265  | 0.8247          |
| 0.8443        | 0.1594 | 270  | 0.8229          |
| 0.8965        | 0.1623 | 275  | 0.8206          |
| 0.8298        | 0.1653 | 280  | 0.8178          |
| 0.7496        | 0.1682 | 285  | 0.8177          |
| 0.7794        | 0.1712 | 290  | 0.8148          |
| 0.8354        | 0.1741 | 295  | 0.8137          |
| 0.8861        | 0.1771 | 300  | 0.8124          |
| 0.7683        | 0.1800 | 305  | 0.8118          |
| 0.8414        | 0.1830 | 310  | 0.8106          |
| 0.8624        | 0.1860 | 315  | 0.8083          |
| 0.7753        | 0.1889 | 320  | 0.8076          |
| 0.778         | 0.1919 | 325  | 0.8060          |
| 0.8171        | 0.1948 | 330  | 0.8051          |
| 0.7006        | 0.1978 | 335  | 0.8049          |
| 0.8365        | 0.2007 | 340  | 0.8032          |
| 0.8057        | 0.2037 | 345  | 0.8021          |
| 0.7914        | 0.2066 | 350  | 0.8015          |
| 0.9043        | 0.2096 | 355  | 0.8008          |
| 0.8317        | 0.2125 | 360  | 0.8001          |
| 0.7631        | 0.2155 | 365  | 0.7997          |
| 0.8301        | 0.2184 | 370  | 0.7993          |
| 0.8701        | 0.2214 | 375  | 0.7988          |
| 0.7469        | 0.2243 | 380  | 0.7985          |
| 0.7643        | 0.2273 | 385  | 0.7981          |
| 0.8388        | 0.2302 | 390  | 0.7978          |
| 0.8808        | 0.2332 | 395  | 0.7975          |
| 0.7441        | 0.2361 | 400  | 0.7974          |
| 0.7641        | 0.2391 | 405  | 0.7972          |
| 0.727         | 0.2420 | 410  | 0.7971          |
| 0.771         | 0.2450 | 415  | 0.7971          |
| 0.7442        | 0.2479 | 420  | 0.7971          |


### Framework versions

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