Update README.md
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README.md
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@@ -7,11 +7,11 @@ To accelerate discovery and guide insights, we report an open-source autoregress
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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.
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```
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from llama_cpp import Llama
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@@ -73,7 +73,6 @@ start_time = time.time()
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result=generate_BioMixtral(system_prompt='You respond accurately.',
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prompt="What is graphene? Answer with detail.",
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max_tokens=512, temperature=0.7, )
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print (result)
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deltat=time.time() - start_time
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print("--- %s seconds ---" % deltat)
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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.
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The model is based on mistralai/Mixtral-8x7B-Instruct-v0.1.
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/K0GifLVENb8G0nERQAzeQ.png)
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This model is based on work reported in https://doi.org/10.1002/advs.202306724, but uses a mixture-of-experts strategy.
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```
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from llama_cpp import Llama
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result=generate_BioMixtral(system_prompt='You respond accurately.',
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prompt="What is graphene? Answer with detail.",
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max_tokens=512, temperature=0.7, )
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print (result)
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deltat=time.time() - start_time
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print("--- %s seconds ---" % deltat)
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