---
library_name: transformers
license: llama2
base_model: lmsys/vicuna-7b-v1.5
tags:
- generated_from_trainer
model-index:
- name: vicuna_7b_stage1_flash
  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. -->

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# vicuna_7b_stage1_flash

This model is a fine-tuned version of [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan

## 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: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 40
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.0038        | 0.0410 | 40   | nan             |
| 2.8811        | 0.0821 | 80   | nan             |
| 2.7369        | 0.1231 | 120  | nan             |
| 2.7568        | 0.1641 | 160  | nan             |
| 2.9021        | 0.2051 | 200  | nan             |
| 2.8989        | 0.2462 | 240  | nan             |
| 2.8846        | 0.2872 | 280  | nan             |
| 2.9066        | 0.3282 | 320  | nan             |
| 2.7595        | 0.3692 | 360  | nan             |
| 2.698         | 0.4103 | 400  | nan             |
| 3.0317        | 0.4513 | 440  | nan             |
| 2.7174        | 0.4923 | 480  | nan             |
| 2.6518        | 0.5333 | 520  | nan             |
| 2.6698        | 0.5744 | 560  | nan             |
| 2.8094        | 0.6154 | 600  | nan             |
| 2.721         | 0.6564 | 640  | nan             |
| 2.679         | 0.6974 | 680  | nan             |
| 2.5793        | 0.7385 | 720  | nan             |
| 2.6417        | 0.7795 | 760  | nan             |
| 2.5729        | 0.8205 | 800  | nan             |
| 2.3099        | 0.8615 | 840  | nan             |
| 2.6534        | 0.9026 | 880  | nan             |
| 2.5593        | 0.9436 | 920  | nan             |
| 2.4978        | 0.9846 | 960  | nan             |
| 2.4152        | 1.0256 | 1000 | nan             |
| 2.44          | 1.0667 | 1040 | nan             |
| 2.5566        | 1.1077 | 1080 | nan             |
| 2.336         | 1.1487 | 1120 | nan             |
| 2.2365        | 1.1897 | 1160 | nan             |
| 2.4516        | 1.2308 | 1200 | nan             |
| 2.3756        | 1.2718 | 1240 | nan             |
| 2.1703        | 1.3128 | 1280 | nan             |
| 2.1208        | 1.3538 | 1320 | nan             |
| 2.2553        | 1.3949 | 1360 | nan             |
| 2.1051        | 1.4359 | 1400 | nan             |
| 2.2217        | 1.4769 | 1440 | nan             |
| 2.0144        | 1.5179 | 1480 | nan             |
| 2.0047        | 1.5590 | 1520 | nan             |
| 2.0336        | 1.6    | 1560 | nan             |
| 1.9599        | 1.6410 | 1600 | nan             |
| 2.0869        | 1.6821 | 1640 | nan             |
| 1.9917        | 1.7231 | 1680 | nan             |
| 2.0273        | 1.7641 | 1720 | nan             |
| 1.7805        | 1.8051 | 1760 | nan             |
| 1.9446        | 1.8462 | 1800 | nan             |
| 1.9071        | 1.8872 | 1840 | nan             |
| 1.9767        | 1.9282 | 1880 | nan             |
| 1.7796        | 1.9692 | 1920 | nan             |
| 1.9373        | 2.0103 | 1960 | nan             |
| 1.6333        | 2.0513 | 2000 | nan             |
| 1.7957        | 2.0923 | 2040 | nan             |
| 1.6708        | 2.1333 | 2080 | nan             |
| 1.7508        | 2.1744 | 2120 | nan             |
| 1.6658        | 2.2154 | 2160 | nan             |
| 1.5108        | 2.2564 | 2200 | nan             |
| 1.6733        | 2.2974 | 2240 | nan             |
| 1.6248        | 2.3385 | 2280 | nan             |
| 1.5717        | 2.3795 | 2320 | nan             |
| 1.6168        | 2.4205 | 2360 | nan             |
| 1.6815        | 2.4615 | 2400 | nan             |
| 1.6728        | 2.5026 | 2440 | nan             |
| 1.6679        | 2.5436 | 2480 | nan             |
| 1.6393        | 2.5846 | 2520 | nan             |
| 1.5578        | 2.6256 | 2560 | nan             |
| 1.5701        | 2.6667 | 2600 | nan             |
| 1.7143        | 2.7077 | 2640 | nan             |
| 1.5139        | 2.7487 | 2680 | nan             |
| 1.7144        | 2.7897 | 2720 | nan             |
| 1.6629        | 2.8308 | 2760 | nan             |
| 1.5304        | 2.8718 | 2800 | nan             |
| 1.5267        | 2.9128 | 2840 | nan             |
| 1.5946        | 2.9538 | 2880 | nan             |
| 1.6313        | 2.9949 | 2920 | nan             |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.6.0.dev20241122+rocm6.2
- Datasets 2.14.7
- Tokenizers 0.20.3