---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.3
model-index:
- name: outputs/axolotl-qlora-out-line
  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)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: true
load_in_4bit: false
strict: false

datasets:
  - path: vdaita/editpackft_inst_line
    type: oasst
dataset_prepared_path: 
val_set_size: 0.05
output_dir: ./outputs/axolotl-qlora-out-line

adapter: lora
lora_model_dir: 

sequence_len: 2048
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules: 
lora_target_linear: true
lora_fan_in_fan_out: 

wandb_project: huggingface
wandb_log_model: axolotl-qlora-line-mistral

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16: 
tf32: false

gradient_checkpointing: true
logging_steps: 1
flash_attention: true

warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1

```

</details><br>

# outputs/axolotl-qlora-out-line

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2883

## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.847         | 0.01  | 1    | 0.9975          |
| 0.3533        | 0.26  | 20   | 0.2772          |
| 0.299         | 0.52  | 40   | 0.2501          |
| 0.2288        | 0.77  | 60   | 0.2439          |
| 0.334         | 1.01  | 80   | 0.2394          |
| 0.3017        | 1.27  | 100  | 0.2399          |
| 0.2394        | 1.53  | 120  | 0.2416          |
| 0.2261        | 1.78  | 140  | 0.2400          |
| 0.177         | 2.02  | 160  | 0.2388          |
| 0.1911        | 2.28  | 180  | 0.2557          |
| 0.1884        | 2.54  | 200  | 0.2601          |
| 0.1516        | 2.79  | 220  | 0.2627          |
| 0.1545        | 3.03  | 240  | 0.2628          |
| 0.092         | 3.29  | 260  | 0.2915          |
| 0.1251        | 3.55  | 280  | 0.2892          |
| 0.109         | 3.8   | 300  | 0.2883          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0