---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: LLM_Rec_Qwen2.5_7B_full_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. -->

# LLM_Rec_Qwen2.5_7B_full_sft

This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the llm_rec dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0229

## 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-06
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 32
- 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: 0.05
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0968        | 0.0746 | 5    | 0.0690          |
| 0.0346        | 0.1493 | 10   | 0.0366          |
| 0.0256        | 0.2239 | 15   | 0.0321          |
| 0.0328        | 0.2985 | 20   | 0.0302          |
| 0.0287        | 0.3731 | 25   | 0.0280          |
| 0.0221        | 0.4478 | 30   | 0.0275          |
| 0.0347        | 0.5224 | 35   | 0.0265          |
| 0.0212        | 0.5970 | 40   | 0.0255          |
| 0.0245        | 0.6716 | 45   | 0.0254          |
| 0.0209        | 0.7463 | 50   | 0.0244          |
| 0.0242        | 0.8209 | 55   | 0.0239          |
| 0.0226        | 0.8955 | 60   | 0.0236          |
| 0.0208        | 0.9701 | 65   | 0.0232          |
| 0.0135        | 1.0448 | 70   | 0.0229          |
| 0.0141        | 1.1194 | 75   | 0.0234          |
| 0.0165        | 1.1940 | 80   | 0.0235          |
| 0.0173        | 1.2687 | 85   | 0.0233          |
| 0.0123        | 1.3433 | 90   | 0.0231          |
| 0.0145        | 1.4179 | 95   | 0.0232          |
| 0.0154        | 1.4925 | 100  | 0.0226          |
| 0.0147        | 1.5672 | 105  | 0.0224          |
| 0.0132        | 1.6418 | 110  | 0.0228          |
| 0.0155        | 1.7164 | 115  | 0.0227          |
| 0.0149        | 1.7910 | 120  | 0.0221          |
| 0.0169        | 1.8657 | 125  | 0.0219          |
| 0.0136        | 1.9403 | 130  | 0.0218          |
| 0.0139        | 2.0149 | 135  | 0.0219          |
| 0.0101        | 2.0896 | 140  | 0.0220          |
| 0.0087        | 2.1642 | 145  | 0.0222          |
| 0.0089        | 2.2388 | 150  | 0.0223          |
| 0.0112        | 2.3134 | 155  | 0.0225          |
| 0.0083        | 2.3881 | 160  | 0.0227          |
| 0.008         | 2.4627 | 165  | 0.0227          |
| 0.0109        | 2.5373 | 170  | 0.0228          |
| 0.0103        | 2.6119 | 175  | 0.0228          |
| 0.008         | 2.6866 | 180  | 0.0229          |
| 0.0103        | 2.7612 | 185  | 0.0229          |
| 0.008         | 2.8358 | 190  | 0.0229          |
| 0.0109        | 2.9104 | 195  | 0.0229          |
| 0.009         | 2.9851 | 200  | 0.0229          |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.5.0
- Datasets 3.1.0
- Tokenizers 0.20.3