peft tensorboard safetensors trl sft generated_from_trainer dataset:generator base_model:mistralai/Mistral-7B-Instruct-v0.2 base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2 license:apache-2.0 region:us
---
base_model: mistralai/Mistral-7B-Instruct-v0.2
datasets:
- generator
library_name: peft
license: apache-2.0
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Translator_Eng_Tel_instruct
  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. -->

# Translator_Eng_Tel_instruct

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2120

## Model description

This model is a fine-tuned version of the Mistral 7B Instruct model aimed at translating English text to Telugu. It has been fine-tuned using the QLoRA 4-bit technique for instruction fine-tuning.

## Intended uses & limitations

This model is intended for translating English text to Telugu. It is recommended to use this model in environments that require high-quality translations between these two languages.

## Usage:
```bash
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM

config = PeftConfig.from_pretrained("MRR24/Translator_Eng_Tel_instruct")
base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
model = PeftModel.from_pretrained(base_model, "MRR24/Translator_Eng_Tel_instruct")
```

## Training and evaluation data

The training dataset consists of 140k data points, while the testing dataset contains 16k data points. These datasets were meticulously curated to ensure the high-quality translation capability of the model.

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.2549        | 0.9992 | 956  | 0.2554          |
| 0.2138        | 1.9984 | 1912 | 0.2120          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1