---
library_name: transformers
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: large_crafting_sft_fail
  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. -->

# large_crafting_sft_fail

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the identity and the large_crafting_sft_fail datasets.
It achieves the following results on the evaluation set:
- Loss: 0.3223

## 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: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5429        | 0.0323 | 50   | 0.4980          |
| 0.5398        | 0.0646 | 100  | 0.4740          |
| 0.5484        | 0.0969 | 150  | 0.4833          |
| 0.5265        | 0.1291 | 200  | 0.4780          |
| 0.5278        | 0.1614 | 250  | 0.4793          |
| 0.5259        | 0.1937 | 300  | 0.4519          |
| 0.5293        | 0.2260 | 350  | 0.4497          |
| 0.5098        | 0.2583 | 400  | 0.4303          |
| 0.482         | 0.2906 | 450  | 0.4249          |
| 0.4683        | 0.3229 | 500  | 0.4224          |
| 0.4572        | 0.3552 | 550  | 0.4136          |
| 0.456         | 0.3874 | 600  | 0.4034          |
| 0.4606        | 0.4197 | 650  | 0.3983          |
| 0.4285        | 0.4520 | 700  | 0.3874          |
| 0.4499        | 0.4843 | 750  | 0.3806          |
| 0.4198        | 0.5166 | 800  | 0.3685          |
| 0.4208        | 0.5489 | 850  | 0.3661          |
| 0.4379        | 0.5812 | 900  | 0.3637          |
| 0.4075        | 0.6134 | 950  | 0.3558          |
| 0.4121        | 0.6457 | 1000 | 0.3513          |
| 0.4112        | 0.6780 | 1050 | 0.3454          |
| 0.4041        | 0.7103 | 1100 | 0.3457          |
| 0.3852        | 0.7426 | 1150 | 0.3384          |
| 0.3656        | 0.7749 | 1200 | 0.3340          |
| 0.384         | 0.8072 | 1250 | 0.3303          |
| 0.3605        | 0.8395 | 1300 | 0.3276          |
| 0.3593        | 0.8717 | 1350 | 0.3247          |
| 0.3624        | 0.9040 | 1400 | 0.3233          |
| 0.3734        | 0.9363 | 1450 | 0.3229          |
| 0.3609        | 0.9686 | 1500 | 0.3223          |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0