generated_from_trainer base_model:mistralai/Mistral-7B-Instruct-v0.1 license:apache-2.0 region:us
---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.1
tags:
- generated_from_trainer
model-index:
- name: Mistral-7B-Instruct-v0.1-LC-PI-.5-noSW
  results: []
---

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# Mistral-7B-Instruct-v0.1-LC-PI-.5-noSW

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8995

## Model description

This model is a fine-tuning of Mistral-7B-Instruct-v0.1.
This FT was done with full attention (removing the 4k SWA).
This FT was using a Position Interpolation factor of 0.5 (Linear RoPE scaling).
Please note that the RoPE scaling factor should be determined by L/L' where L is the pre-training max context length and L' is the new max context length. In our case, we are just making experiments (and for us we would have had L/L' = 8096/7200 > 1 which did not require any PI scaling).

## Intended uses & limitations

More information needed

## Training and evaluation data

Data is a 9k sample from the RedPajama datset. The context is <=7200 with a decreasing exponential distribution of scale 1500.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- training_steps: 300

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1056        | 0.18  | 50   | 1.9680          |
| 2.1266        | 0.36  | 100  | 1.9213          |
| 1.978         | 0.55  | 150  | 1.9084          |
| 1.8576        | 0.73  | 200  | 1.9022          |
| 2.0311        | 0.91  | 250  | 1.8999          |
| 1.9197        | 1.09  | 300  | 1.8995          |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.0+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
