---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:
- generated_from_trainer
datasets:
- gohsyi/metamath-sft
metrics:
- accuracy
model-index:
- name: Llama-3.2-1B-Instruct-sft_metamath
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: gohsyi/metamath-sft
      type: gohsyi/metamath-sft
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8814735253307663
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Llama-3.2-1B-Instruct-sft_metamath

This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the gohsyi/metamath-sft dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4330
- Accuracy: 0.8815

## 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: 14
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 16
- total_train_batch_size: 448
- total_eval_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0

### Training results



### Framework versions

- Transformers 4.46.2
- Pytorch 2.3.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3