---
license: other
base_model: Meta-Llama-3.1-8B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: all_abla_numina_oly_orca
  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. -->

# all_abla_numina_oly_orca

This model is a fine-tuned version of [/home/test/testdata/models/Meta-Llama-3.1-8B-Instruct](https://huggingface.co//home/test/testdata/models/Meta-Llama-3.1-8B-Instruct) on the codefeedback-o1, the magicoder-o1, the magicoder-oss-o1, the mathinstruct-MATH-o1, the mathinstruct-augmented-o1, the numina-cn-k12-o1, the numina-not-cn-k12-o1, the reasoning-001-o1 and the ultramedical_mc_o1 datasets.
It achieves the following results on the evaluation set:
- Loss: 0.2059
- Accuracy: 0.9286

## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- total_eval_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: 3.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2107        | 0.5574 | 500  | 0.2142          | 0.9208   |
| 0.1561        | 1.1148 | 1000 | 0.2085          | 0.9239   |
| 0.1547        | 1.6722 | 1500 | 0.1994          | 0.9265   |
| 0.1092        | 2.2297 | 2000 | 0.2073          | 0.9278   |
| 0.1073        | 2.7871 | 2500 | 0.2064          | 0.9284   |


### Framework versions

- Transformers 4.43.4
- Pytorch 2.4.0
- Datasets 2.20.0
- Tokenizers 0.19.1