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---
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
datasets:
- barc0/induction_20k_gpt4o-mini_generated_problems_seed100.jsonl_messages_format_0.3
library_name: peft
license: llama3.1
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
model-index:
- name: induction-run4-gpt4omini-20k-100seeds-barc-llama3.1-8b-instruct-lora64_lr2e-4_epoch3
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. -->
# induction-run4-gpt4omini-20k-100seeds-barc-llama3.1-8b-instruct-lora64_lr2e-4_epoch3
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on the barc0/induction_20k_gpt4o-mini_generated_problems_seed100.jsonl_messages_format_0.3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3360
## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 4
- seed: 4
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.3484 | 0.9966 | 145 | 0.3561 |
| 0.3202 | 2.0 | 291 | 0.3388 |
| 0.3107 | 2.9897 | 435 | 0.3360 |
### Framework versions
- PEFT 0.13.0
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1 |