---
library_name: transformers
license: apache-2.0
base_model: amd/AMD-Llama-135m
tags:
- generated_from_trainer
model-index:
- name: amdchess-v8
  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-v8

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: 0.7861

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 3.3296        | 0.0030 | 5    | 2.6555          |
| 1.7617        | 0.0059 | 10   | 1.6829          |
| 1.344         | 0.0089 | 15   | 1.3500          |
| 1.1587        | 0.0118 | 20   | 1.1881          |
| 1.1949        | 0.0148 | 25   | 1.1602          |
| 1.0248        | 0.0177 | 30   | 1.1076          |
| 1.1176        | 0.0207 | 35   | 1.1118          |
| 0.9583        | 0.0236 | 40   | 1.0467          |
| 1.1116        | 0.0266 | 45   | 1.0376          |
| 0.9633        | 0.0295 | 50   | 1.0231          |
| 0.9704        | 0.0325 | 55   | 1.0089          |
| 1.0535        | 0.0354 | 60   | 1.0089          |
| 0.9668        | 0.0384 | 65   | 0.9763          |
| 0.9767        | 0.0413 | 70   | 0.9681          |
| 0.9745        | 0.0443 | 75   | 0.9648          |
| 0.9497        | 0.0472 | 80   | 0.9631          |
| 0.9192        | 0.0502 | 85   | 0.9406          |
| 0.9581        | 0.0531 | 90   | 0.9435          |
| 0.8981        | 0.0561 | 95   | 0.9271          |
| 0.9811        | 0.0590 | 100  | 0.9287          |
| 0.8313        | 0.0620 | 105  | 0.9138          |
| 0.898         | 0.0649 | 110  | 0.9120          |
| 0.954         | 0.0679 | 115  | 0.9109          |
| 0.9523        | 0.0708 | 120  | 0.9067          |
| 0.948         | 0.0738 | 125  | 0.9001          |
| 0.8825        | 0.0767 | 130  | 0.8932          |
| 0.9259        | 0.0797 | 135  | 0.8908          |
| 0.7937        | 0.0826 | 140  | 0.8831          |
| 0.8315        | 0.0856 | 145  | 0.8794          |
| 0.8488        | 0.0885 | 150  | 0.8800          |
| 0.8648        | 0.0915 | 155  | 0.8726          |
| 0.8976        | 0.0945 | 160  | 0.8701          |
| 0.9298        | 0.0974 | 165  | 0.8650          |
| 0.8856        | 0.1004 | 170  | 0.8635          |
| 0.7848        | 0.1033 | 175  | 0.8584          |
| 0.8366        | 0.1063 | 180  | 0.8526          |
| 0.8413        | 0.1092 | 185  | 0.8531          |
| 0.8577        | 0.1122 | 190  | 0.8498          |
| 0.8641        | 0.1151 | 195  | 0.8457          |
| 0.7957        | 0.1181 | 200  | 0.8429          |
| 0.8379        | 0.1210 | 205  | 0.8454          |
| 0.7596        | 0.1240 | 210  | 0.8404          |
| 0.8703        | 0.1269 | 215  | 0.8390          |
| 0.7297        | 0.1299 | 220  | 0.8327          |
| 0.885         | 0.1328 | 225  | 0.8299          |
| 0.7785        | 0.1358 | 230  | 0.8300          |
| 0.851         | 0.1387 | 235  | 0.8264          |
| 0.7234        | 0.1417 | 240  | 0.8222          |
| 0.7917        | 0.1446 | 245  | 0.8226          |
| 0.8123        | 0.1476 | 250  | 0.8195          |
| 0.7801        | 0.1505 | 255  | 0.8170          |
| 0.7086        | 0.1535 | 260  | 0.8156          |
| 0.8673        | 0.1564 | 265  | 0.8137          |
| 0.8298        | 0.1594 | 270  | 0.8144          |
| 0.8097        | 0.1623 | 275  | 0.8113          |
| 0.8079        | 0.1653 | 280  | 0.8095          |
| 0.7917        | 0.1682 | 285  | 0.8079          |
| 0.8206        | 0.1712 | 290  | 0.8058          |
| 0.8438        | 0.1741 | 295  | 0.8037          |
| 0.8519        | 0.1771 | 300  | 0.8015          |
| 0.8844        | 0.1800 | 305  | 0.8016          |
| 0.8217        | 0.1830 | 310  | 0.7998          |
| 0.6939        | 0.1860 | 315  | 0.7982          |
| 0.8021        | 0.1889 | 320  | 0.7975          |
| 0.8357        | 0.1919 | 325  | 0.7961          |
| 0.8487        | 0.1948 | 330  | 0.7945          |
| 0.648         | 0.1978 | 335  | 0.7936          |
| 0.7599        | 0.2007 | 340  | 0.7924          |
| 0.8203        | 0.2037 | 345  | 0.7923          |
| 0.8072        | 0.2066 | 350  | 0.7915          |
| 0.8278        | 0.2096 | 355  | 0.7904          |
| 0.7202        | 0.2125 | 360  | 0.7898          |
| 0.7229        | 0.2155 | 365  | 0.7891          |
| 0.8432        | 0.2184 | 370  | 0.7887          |
| 0.8615        | 0.2214 | 375  | 0.7879          |
| 0.8234        | 0.2243 | 380  | 0.7875          |
| 0.8101        | 0.2273 | 385  | 0.7871          |
| 0.8464        | 0.2302 | 390  | 0.7868          |
| 0.7966        | 0.2332 | 395  | 0.7866          |
| 0.718         | 0.2361 | 400  | 0.7864          |
| 0.741         | 0.2391 | 405  | 0.7863          |
| 0.7903        | 0.2420 | 410  | 0.7862          |
| 0.7671        | 0.2450 | 415  | 0.7861          |
| 0.7657        | 0.2479 | 420  | 0.7861          |


### Framework versions

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