README.md

---
base_model: lamm-mit/Bioinspired-Phi-3-mini-4k
inference: false
language:
- en
library_name: transformers
model_creator: lamm-mit
model_name: Bioinspired-Phi-3-mini-4k
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- biology
- materials science
- code
- scientific AI
- biological materials
- bioinspiration
- machine learning
- generative
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
---
# lamm-mit/Bioinspired-Phi-3-mini-4k-GGUF

Quantized GGUF model files for [Bioinspired-Phi-3-mini-4k](https://huggingface.co/lamm-mit/Bioinspired-Phi-3-mini-4k) from [lamm-mit](https://huggingface.co/lamm-mit)


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [bioinspired-phi-3-mini-4k.fp16.gguf](https://huggingface.co/afrideva/Bioinspired-Phi-3-mini-4k-GGUF/resolve/main/bioinspired-phi-3-mini-4k.fp16.gguf) | fp16 | 7.64 GB  |
| [bioinspired-phi-3-mini-4k.q2_k.gguf](https://huggingface.co/afrideva/Bioinspired-Phi-3-mini-4k-GGUF/resolve/main/bioinspired-phi-3-mini-4k.q2_k.gguf) | q2_k | 1.42 GB  |
| [bioinspired-phi-3-mini-4k.q3_k_m.gguf](https://huggingface.co/afrideva/Bioinspired-Phi-3-mini-4k-GGUF/resolve/main/bioinspired-phi-3-mini-4k.q3_k_m.gguf) | q3_k_m | 1.96 GB  |
| [bioinspired-phi-3-mini-4k.q4_k_m.gguf](https://huggingface.co/afrideva/Bioinspired-Phi-3-mini-4k-GGUF/resolve/main/bioinspired-phi-3-mini-4k.q4_k_m.gguf) | q4_k_m | 2.39 GB  |
| [bioinspired-phi-3-mini-4k.q5_k_m.gguf](https://huggingface.co/afrideva/Bioinspired-Phi-3-mini-4k-GGUF/resolve/main/bioinspired-phi-3-mini-4k.q5_k_m.gguf) | q5_k_m | 2.82 GB  |
| [bioinspired-phi-3-mini-4k.q6_k.gguf](https://huggingface.co/afrideva/Bioinspired-Phi-3-mini-4k-GGUF/resolve/main/bioinspired-phi-3-mini-4k.q6_k.gguf) | q6_k | 3.14 GB  |
| [bioinspired-phi-3-mini-4k.q8_0.gguf](https://huggingface.co/afrideva/Bioinspired-Phi-3-mini-4k-GGUF/resolve/main/bioinspired-phi-3-mini-4k.q8_0.gguf) | q8_0 | 4.06 GB  |



## Original Model Card:
# 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

Abstract: The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge is systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model is finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further strengthened with enhanced reasoning ability, as well as with Retrieval-Augmented Generation (RAG) to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model shows impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains. 

![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/Xdp_nCYiF2IAPamG5ffIC.png)

# Model Card for Model ID

Fine-tuned LLM with domain knowledge in biological materials, mechanics of materials, modeling and simulation, and related fields. 



## Model Details

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## Training Details

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