
# Model Description
This merge of lora model was finetuned using  The base model [sarvamai/OpenHathi-7B-Hi-v0.1-Base](https://huggingface.co/sarvamai/OpenHathi-7B-Hi-v0.1-Base) using [Unsloth](https://github.com/unslothai/unsloth)
This model can translate from english to hindi and hindi to english 
<img src="https://cdn-uploads.huggingface.co/production/uploads/6487239cca30096ea9f52115/Rsixw_aSB-ytZT7VEQ06c.jpeg" width="500" height="500" alt="Image">

# Steps to try the model :

## Load the model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("damerajee/openhathi-h2e-e2h")
model = AutoModelForCausalLM.from_pretrained("damerajee/openhathi-h2e-e2h")
```
## Inference

### For english to hindi(e2h)
```python
inputs = tokenizer(["[INST]translate this from english to hindi: Be a free thinker and don't accept everything you hear as truth. Be critical and evaluate what you believe in. [/INST]<s> hindi output:"]*1, return_tensors = "pt")

outputs = model.generate(**inputs, max_new_tokens = 18, use_cache = True)
tokenizer.batch_decode(outputs)

```
### For hindi to english(h2e)
```python
inputs = tokenizer(["[INST]translate this from hindi to english: अगर तुम सूरज की तरह चमकना चाहते हो, तो सूरज की तरह जलना सीखो।[/INST]<s> english output:"]*1, return_tensors = "pt")

outputs = model.generate(**inputs, max_new_tokens = 18, use_cache = True)
tokenizer.batch_decode(outputs)
```


# Dataset 
* The dataset used was the combination of two dataset which gave a total of 1_786_788 rows 
* The rows were then pre-process to look something like this :

```python
[INST]translate this from english to hindi: When it is said to him: \'Fear Allah\' egotism takes him in his sin. Gehenna (Hell) shall be enough for him. How evil a cradling! [/INST] hindi output: और जब उससे कहा जाता है,
"अल्लाह से डर", तो अहंकार उसे और गुनाह पर जमा देता है। अतः उसके लिए तो जहन्नम ही काफ़ी है, और वह बहुत-ही बुरी शय्या है! '
```
* This was done for both english to hindi and  hindi to english hence the name h2e and e2h
* Now when doing the above we get a total of 3 million plus rows

# Training details
* The model was loaded in 4-Bit
* The target modules include "q_proj", "k_proj", "v_proj", "o_proj"
* The fine-tuning was done on a free goggle colab with a single t4 GPU (huge thanks to unsloth for this)
* Even though the Full dataset was almost 3 million The lora model was finetuned on only 1 million row for each language
  
# Limitations
The model was not fully trained on all the dataset and Much evaluation was not done so any contributions will be helpful.

As of right now this is a smaller model Better model trained on better dataset will be released
{"language": ["en", "hi"], "license": "apache-2.0", "library_name": "transformers", "tags": ["translation", "Bilingual"], "datasets": ["Aarif1430/english-to-hindi", "Sampuran01/english-hindi-translation"], "metrics": ["bleu"], "pipeline_tag": "translation"}
transformers safetensors llama text-generation translation Bilingual en hi dataset:Aarif1430/english-to-hindi dataset:Sampuran01/english-hindi-translation license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us