Update README.md

README.md

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 ## Model Description
 
-Palmyra Base was primarily pre-trained with English text. Note that there is still a trace amount of non-English data present within the training corpus that was accessed through CommonCrawl. A causal language modeling (CLM) objective was utilized during the process of the model's pretraining. Similar to GPT-3, Palmyra Base is a member of the same family of models that only contain a decoder. As a result, it was pre-trained utilizing the objective of self-supervised causal language modeling. Palmyra Base uses the prompts and general experimental setup from GPT-3 in order to conduct its evaluation per GPT-3.
+Camel-5b is a trained large language model that follows instructions. Based on [Palmyra-Base](https://huggingface.co/Writer/palmyra-base) is trained on ~70k instruction & response fine tuning records generated by Writer Team from the InstructGPT paper, including brainstorming, classification, closed quality assurance, generation, information extraction, open quality assurance, and summarization.
 
 
 
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 ### Limitations and Biases
 
-Palmyra Base’s core functionality is to take a string of text and predict the next token. While language models are widely used for other tasks, there are many unknowns in this work. When prompting Palmyra Base, keep in mind that the next statistically likely token is not always the token that produces the most "accurate" text. Never rely on Palmyra Base to produce factually correct results.
+Camel's core functionality is to take a string of text and predict the next token. While language models are widely used for other tasks, there are many unknowns in this work. When prompting Camel, keep in mind that the next statistically likely token is not always the token that produces the most "accurate" text. Never rely on Camel to produce factually correct results.
 
-Palmyra Base was trained on Writer’s custom data. As with all language models, it is difficult to predict how Palmyra Base will respond to specific prompts, and offensive content may appear unexpectedly. We recommend that the outputs be curated or filtered by humans before they are released, both to censor undesirable content and to improve the quality of the results.
+Camel was trained on Writer’s custom data. As with all language models, it is difficult to predict how Camel will respond to specific prompts, and offensive content may appear unexpectedly. We recommend that the outputs be curated or filtered by humans before they are released, both to censor undesirable content and to improve the quality of the results.
 
 
 ## Evaluation results
 
-Evaluation of Palmyra-base model on the benchmark
+Evaluation of Camel-5B model on the benchmark
 
 Coming Soon