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---
library_name: transformers
license: llama3.2
base_model: NousResearch/Llama-3.2-1B
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrachat_200k
model-index:
- name: llama-3-2-1b-sft
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. -->
# llama-3-2-1b-sft
This model is a fine-tuned version of [NousResearch/Llama-3.2-1B](https://huggingface.co/NousResearch/Llama-3.2-1B) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2759
See the training yaml https://github.com/wassname/SimPO/blob/main/training_configs/llama-3-2-1b-base-sft.yaml
## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3663 | 0.0534 | 200 | 1.3955 |
| 1.3413 | 0.1069 | 400 | 1.3722 |
| 1.365 | 0.1603 | 600 | 1.3632 |
| 1.33 | 0.2138 | 800 | 1.3532 |
| 1.3219 | 0.2672 | 1000 | 1.3463 |
| 1.3355 | 0.3207 | 1200 | 1.3391 |
| 1.334 | 0.3741 | 1400 | 1.3305 |
| 1.3183 | 0.4276 | 1600 | 1.3233 |
| 1.334 | 0.4810 | 1800 | 1.3161 |
| 1.3013 | 0.5345 | 2000 | 1.3087 |
| 1.3156 | 0.5879 | 2200 | 1.3016 |
| 1.3092 | 0.6414 | 2400 | 1.2953 |
| 1.2518 | 0.6948 | 2600 | 1.2895 |
| 1.2617 | 0.7483 | 2800 | 1.2846 |
| 1.3041 | 0.8017 | 3000 | 1.2809 |
| 1.3102 | 0.8552 | 3200 | 1.2781 |
| 1.2675 | 0.9086 | 3400 | 1.2765 |
| 1.2978 | 0.9621 | 3600 | 1.2759 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
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