Rename Readme.md to README.md

Readme.md → README.md

@@ -9,36 +9,36 @@ language:
   - en
 ---
 widget:
-  - text: >
-      <|system|>
-
-      You are a chatbot who can help code!</s>
-
-      <|user|>
+- text: "<|system|>\nYou are a chatbot who can help code!</s>\n<|user|>\nWrite me a function to calculate the first 10 digits of the fibonacci sequence in Python and print it out to the CLI.</s>\n<|assistant|>\n"
+---
 <div align="center">
 
-#TinyLlama-1.1B
+# TinyLlama-1.1B
+</div>
+
 https://github.com/jzhang38/TinyLlama
 
-The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.
+The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01. 
+
 
 We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
 
-This Model
-This is the chat model finetuned on top of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T. We follow HF's Zephyr's training recipe. The model was " initially fine-tuned on a variant of the UltraChat dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with 🤗 TRL's DPOTrainer on the openbmb/UltraFeedback dataset, which contain 64k prompts and model completions that are ranked by GPT-4."
+#### This Model
+This is the chat model finetuned on top of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T). **We follow [HF's Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/edit/main/README.md)'s training recipe.** The model was " initially fine-tuned on a variant of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. 
+We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contain 64k prompts and model completions that are ranked by GPT-4." 
 
-How to use
-You will need the transformers>=4.34 Do check the TinyLlama github page for more information.
 
+#### How to use
+You will need the transformers>=4.34
+Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.
+
+```python
 # Install transformers from source - only needed for versions <= v4.34
 # pip install git+https://github.com/huggingface/transformers.git
 # pip install accelerate
-
 import torch
 from transformers import pipeline
-
 pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16, device_map="auto")
-
 # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
 messages = [
     {
@@ -55,4 +55,5 @@ print(outputs[0]["generated_text"])
 # <|user|>
 # How many helicopters can a human eat in one sitting?</s>
 # <|assistant|>
-# ...
+# ...
+```