---
library_name: transformers
license: llama3.1
base_model: meta-llama/Llama-3.1-8B
tags:
- llama-factory
- full
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Llama-3.1-8B-SFT-envbench_weave_2500
  results: []
---

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# Llama-3.1-8B-SFT-envbench_weave_2500

This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) on the envbench_weave_2500 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3857
- Accuracy: 0.8937
- Num Input Tokens Seen: 93186816

## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0

### Training results



### Framework versions

- Transformers 4.52.4
- Pytorch 2.6.0a0+df5bbc09d1.nv24.12
- Datasets 3.6.0
- Tokenizers 0.21.1