Create README.md

README.md

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+---
+license: mit
+language:
+- en
+- kn
+metrics:
+- accuracy
+pipeline_tag: text-generation
+tags:
+- bilingual
+- kannada
+- english
+---
+
+(This repo contains the sharded version of the [original](https://huggingface.co/Cognitive-Lab/Ambari-7B-base-v0.1) Ambari-7B model)
+
+# Ambari-7B-Base-v0.1 (sharded)
+
+## Overview
+
+Ambari-7B-Base-v0.1 is the first bilingual English/Kannada model in the Ambari series, developed and released by [Cognitivelab.in](https://www.cognitivelab.in/). Based on the Llama2 model by Meta, this 7B parameter model is the outcome of the pretraining stage, involving training on approximately 500 million new Kannada tokens.
+
+## Usage
+
+To use the Ambari-7B-Base-v0.1 model, you can follow the example code below:
+
+```python
+# Usage
+import torch
+from transformers import LlamaTokenizer, LlamaForCausalLM
+
+model = LlamaForCausalLM.from_pretrained('Cognitive-Lab/Ambari-7B-Base-v0.1')
+tokenizer = LlamaTokenizer.from_pretrained('Cognitive-Lab/Ambari-7B-Base-v0.1')
+
+prompt = "ಕನ್ನಡದ ಇತಿಹಾಸವನ್ನು ವಿವರವಾಗಿ ತಿಳಿಸಿ"
+inputs = tokenizer(prompt, return_tensors="pt")
+
+# Generate
+generate_ids = model.generate(inputs.input_ids, max_length=30)
+decoded_output = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+
+print(decoded_output)
+```
+
+**Important:** The provided model serves as a foundation and is not designed for independent use. We strongly advise conducting finetuning tailored to your particular task(s) of interest before deploying it in a production environment. Feel free to customize the code according to your specific use case, ensuring that the model undergoes finetuning for optimal performance in your desired application.