Update README.md

README.md

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+---
+language:
+- en
+tags:
+- biology
+- text-generation-inference
+---
 # BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials
 
 Reference: R. Luu and M.J. Buehler, "BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials," Adv. Science, 2023, DOI: https://doi.org/10.1002/advs.202306724
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 ### Notes and licenses
 
-This model was fine-tuned based on: https://huggingface.co/microsoft/Orca-2-13b (details in https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202306724)/
+BioinspiredLLM was fine-tuned based on: https://huggingface.co/microsoft/Orca-2-13b. Details see: https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202306724
 
 Orca 2 is licensed under the Microsoft Research License (https://huggingface.co/microsoft/Orca-2-13b/blob/main/LICENSE).
 
@@ -209,9 +216,9 @@ Llama 2 is licensed under the LLAMA 2 Community License (https://ai.meta.com/lla
 
 #### Bias, Risks, and Limitations
 
-Like in all techniques of modeling, there are possibilities of errors. The base models Llama 2 and Orca 2 models were aligned to not spread misinformation and produce safer responses. As a result, BioinspiredLLM has inherited these traits and performs reasonably well in these dimensions. However, it is still of utmost importance for researchers to also verify responses and avoid propagating errors, as discussed in recent literature[64] – a standard practice across all modeling techniques. To minimize risk of mistakes, employing chain-of-thought prompting and RAG methods, as introduced, proves beneficial. Additionally, the system prompt of BioinspiredLLM can be edited to guide context. Further details see the main paper.
+Like in all techniques of modeling, there are possibilities of errors. The base models Llama 2 and Orca 2 models were aligned to not spread misinformation and produce safer responses.  As a result, BioinspiredLLM has inherited these traits and performs reasonably well in these dimensions.  However, it is still of utmost importance for researchers to also verify responses and avoid propagating errors. To minimize risk of mistakes, employing chain-of-thought prompting and RAG methods, as introduced, proves beneficial. Additionally, the system prompt of BioinspiredLLM can be edited to guide context. Further details see the main paper.
 
-This model, built upon the LLaMA 2 and Orca-2 model family, retains many of its limitations, as well as the common limitations of other large language models or limitation caused by its training process, including:
+BioinspiredLLM, built upon the LLaMA 2 and Orca-2 model family, retains many of its limitations, as well as the common limitations of other large language models or limitation caused by its training process, including:
 
 Data Biases: Large language models, trained on extensive data, can inadvertently carry biases present in the source data. Consequently, the models may generate outputs that could be potentially biased or unfair.
 
@@ -225,4 +232,4 @@ Hallucination: It is important to be aware and cautious not to entirely rely on
 
 Potential for Misuse: Without suitable safeguards, there is a risk that these models could be maliciously used for generating disinformation or harmful content.
  
-This model is solely designed for research settings, and its testing has only been carried out in such environments. It should not be used in downstream applications, as additional analysis is needed to assess potential harm or bias in the proposed application.
+This model is solely designed for research settings, and its testing has only been carried out in such environments. It should not be used in downstream applications, as additional analysis is needed to assess potential harm or bias in the proposed application.