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---
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
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