| --- |
| library_name: transformers |
| base_model: sengi/lladou-gsm8k-b32 |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: LLaDA-8B-Instruct |
| 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. --> |
|
|
| # LLaDA-8B-Instruct |
|
|
| This model is a fine-tuned version of [sengi/lladou-gsm8k-b32](https://huggingface.co/sengi/lladou-gsm8k-b32) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: -9.4135 |
|
|
| ## 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-05 |
| - train_batch_size: 10 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - distributed_type: multi-GPU |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 40 |
| - optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_steps: 200 |
| - training_steps: 10000 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:------:|:-----:|:---------------:| |
| | 12.5223 | 0.001 | 1000 | 19.8564 | |
| | 15.1946 | 0.002 | 2000 | 20.4100 | |
| | -13.4932 | 0.3 | 3000 | -12.4603 | |
| | -12.5083 | 0.4 | 4000 | -9.9804 | |
| | -11.9398 | 0.5 | 5000 | -9.1448 | |
| | -15.111 | 1.0532 | 6000 | -9.1216 | |
| | -16.8137 | 1.1532 | 7000 | -9.8617 | |
| | -14.6685 | 1.2532 | 8000 | -10.7716 | |
| | -13.1242 | 1.3532 | 9000 | -9.1976 | |
| | -12.7972 | 1.4532 | 10000 | -9.4135 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.56.1 |
| - Pytorch 2.8.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.0 |
|
|
