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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+gpt-2-tamil - bnb 8bits
+- Model creator: https://huggingface.co/abinayam/
+- Original model: https://huggingface.co/abinayam/gpt-2-tamil/
+
+
+
+
+Original model description:
+---
+
+language: ta
+datasets:
+- oscar
+- IndicNLP
+widget:
+- text: 'ஒரு ஊரிலே ஒரு காக்கைக்கு'
+
+---
+# GPT2-Tamil
+
+This repository is created as part of the Flax/Jax community week by Huggingface. The aim of this project is to pretrain a language model using GPT-2 specifically for Tamil language. 
+
+## Setup:
+To setup the project, run the following command,
+```python
+pip install -r requirements.txt
+```
+
+## Model:
+Pretrained model on Tamil language using a causal language modeling (CLM) objective.
+ 
+## Dataset Used:
+The GTP-2 model is trained on [oscar dataset - ta](https://huggingface.co/datasets/oscar) and [IndicNLP dataset - ta](https://indicnlp.ai4bharat.org/corpora/)
+
+## Intended uses & limitations:
+You can use the raw model for next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
+
+## How to pretrain the model:
+To perform training, do the following steps,
+
+- Export the model directory (where you want to store the model artifacts like config, tokenizer, etc.)
+```python
+>>> export MODEL_DIR=<model_dir>
+```
+- Create the config.json by running the following command,
+```python
+>>> python src/create_config.py
+```
+- Create the tokenizer by running the following command,
+```python
+>>> python src/train_tokenizer.py
+```
+- Once the config and tokenizer is created, run the following script to start training the flax model
+```python
+>>> python scripts/train_gpt2-oscar-tamil.sh
+```
+
+## How to use:
+To perform language generation using the model, pipeline can be used directly.
+
+- First convert the flax model to pytorch using the following command,
+```python
+python src/convert_flax_to_pytorch.py
+```
+- Use the following snippet to perform language generation,
+```python
+ >>> from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
+ >>> model_name = 'abinayam/gpt-2-tamil'
+ >>> model = AutoModelWithLMHead.from_pretrained(model_name)
+ >>> tokenizer = AutoTokenizer.from_pretrained(model_name)
+ >>> set_seed(42)
+ >>> input_text = "ஒரு ஊரிலே ஒரு காக்கைக்கு"
+ >>> max_len = 300
+ >>> no_seq = 5
+ >>> generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
+ >>> sequence = generator(input_text, max_length=max_len, num_return_sequences=no_seq)
+```
+
+