---
library_name: transformers
license: other
base_model: /nfs/stak/users/gautammi/my-hpc-share/workspace/research/research/RNADesign_Mine/models/Qwen2.5-0.5B-RNA
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: sft
  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. -->

# sft

This model is a fine-tuned version of [/nfs/stak/users/gautammi/my-hpc-share/workspace/research/research/RNADesign_Mine/models/Qwen2.5-0.5B-RNA](https://huggingface.co//nfs/stak/users/gautammi/my-hpc-share/workspace/research/research/RNADesign_Mine/models/Qwen2.5-0.5B-RNA) on the V4_phase2_train dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4317

## 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: 256
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 512
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.1666        | 0.0218 | 100  | 1.1604          |
| 1.1794        | 0.0436 | 200  | 1.1023          |
| 1.1542        | 0.0654 | 300  | 1.0963          |
| 1.1265        | 0.0871 | 400  | 1.0635          |
| 0.8963        | 0.1089 | 500  | 0.8135          |
| 0.7587        | 0.1307 | 600  | 0.6705          |
| 0.6947        | 0.1525 | 700  | 0.6066          |
| 0.5813        | 0.1743 | 800  | 0.5263          |
| 0.5319        | 0.1961 | 900  | 0.4963          |
| 0.503         | 0.2178 | 1000 | 0.4993          |
| 0.4843        | 0.2396 | 1100 | 0.4703          |
| 0.4691        | 0.2614 | 1200 | 0.4688          |
| 0.4602        | 0.2832 | 1300 | 0.4593          |
| 0.4495        | 0.3050 | 1400 | 0.4542          |
| 0.4435        | 0.3268 | 1500 | 0.4499          |
| 0.4351        | 0.3485 | 1600 | 0.4446          |
| 0.4335        | 0.3703 | 1700 | 0.4409          |
| 0.4259        | 0.3921 | 1800 | 0.4384          |
| 0.4254        | 0.4139 | 1900 | 0.4347          |
| 0.4193        | 0.4357 | 2000 | 0.4358          |
| 0.4164        | 0.4575 | 2100 | 0.4329          |
| 0.4142        | 0.4793 | 2200 | 0.4327          |
| 0.4119        | 0.5010 | 2300 | 0.4287          |
| 0.4109        | 0.5228 | 2400 | 0.4288          |
| 0.4117        | 0.5446 | 2500 | 0.4306          |
| 0.4073        | 0.5664 | 2600 | 0.4350          |
| 0.4062        | 0.5882 | 2700 | 0.4280          |
| 0.4037        | 0.6100 | 2800 | 0.4277          |
| 0.4054        | 0.6317 | 2900 | 0.4272          |
| 0.4031        | 0.6535 | 3000 | 0.4284          |
| 0.3998        | 0.6753 | 3100 | 0.4282          |
| 0.4003        | 0.6971 | 3200 | 0.4296          |
| 0.4021        | 0.7189 | 3300 | 0.4282          |
| 0.3982        | 0.7407 | 3400 | 0.4296          |
| 0.3988        | 0.7624 | 3500 | 0.4298          |
| 0.3988        | 0.7842 | 3600 | 0.4299          |
| 0.3949        | 0.8060 | 3700 | 0.4309          |
| 0.3961        | 0.8278 | 3800 | 0.4298          |
| 0.3952        | 0.8496 | 3900 | 0.4307          |
| 0.397         | 0.8714 | 4000 | 0.4310          |
| 0.3935        | 0.8931 | 4100 | 0.4307          |
| 0.3931        | 0.9149 | 4200 | 0.4322          |
| 0.3942        | 0.9367 | 4300 | 0.4313          |
| 0.3951        | 0.9585 | 4400 | 0.4317          |
| 0.3922        | 0.9803 | 4500 | 0.4317          |


### Framework versions

- Transformers 4.56.1
- Pytorch 2.4.1+cu121
- Datasets 4.0.0
- Tokenizers 0.22.0