Create README.md

README.md

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+Sure! Here's the updated README markdown with the Apache v2 license:
+
+# Mpt-7B-Assistant
+
+[![License: Apache v2](https://img.shields.io/badge/License-Apache%20v2-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+
+Mpt-7B-Assistant is an AI assistant built using Flax/JAX and trained on Cloud TPUs. The model has a context length of 5144 and 7B parameters, making it suitable for a wide range of natural language processing tasks.
+
+## Usage
+
+Once you have installed the repository, you can start using the model by importing it into your Python code:
+
+```python
+from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("huggingface/Mpt-7B-Assistant")
+model = FlaxAutoModelForCausalLM.from_pretrained("huggingface/Mpt-7B-Assistant")
+
+prompt = "<|endoftext|><|prompter|>Hello, how are you today?<|endoftext|><|assistant|>"
+input_ids = tokenizer.encode(prompt, return_tensors="jax")
+
+output_ids = model.generate(input_ids, max_length=100)
+output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+print(output_text)
+```
+
+This will generate a response from the model based on the input prompt. You can customize the length of the generated output by changing the `max_length` parameter in the `generate()` method.
+
+## Training
+
+The Mpt-7B-Assistant model was trained using EasyDel and OST-OpenSourceTransformers library, which provides an easy-to-use interface for training large language models on Cloud TPUs.
+
+This will train the model on the specified dataset using Cloud TPUs, and save the trained model to the specified output directory.
+
+## License
+
+Mpt-7B-Assistant is licensed under the Apache v2 License. See `LICENSE` for more information.