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datasets: |
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- S2ORC |
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language: |
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- en |
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tags: |
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- llama |
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- ggml |
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- pubmed |
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- medicine |
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- research |
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- papers |
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--- |
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# --- |
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--- |
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# PMC_LLaMA - finetuned on PubMed Central papers |
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**This is a ggml conversion of chaoyi-wu's [PMC_LLAMA_7B_10_epoch](https://huggingface.co/chaoyi-wu/PMC_LLAMA_7B_10_epoch) model.** |
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**It is a LLaMA model which is finetuned on PubMed Central papers from** |
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**The Semantic Scholar Open Research Coprus [dataset](https://github.com/allenai/s2orc).** |
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Currently I have only converted it into **new k-quant method Q5_K_M**. I will gladly make more versions on request. |
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Other possible quantizations include: q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q5_K_M, q6_K |
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Compatible with **llama.cpp**, but also with: |
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- **text-generation-webui** |
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- **KoboldCpp** |
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- **ParisNeo/GPT4All-UI** |
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- **llama-cpp-python** |
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- **ctransformers** |
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--- |
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# CAVE! |
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Being a professional myself and having tested the model, I can strongly advise that this model is best left in the hands of professionals. |
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This model can produce very detailed and elaborate responses, but it tends to confabulate quite often in my opinion (considering the field of use). |
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Because of the detail accuracy, it is difficult for a layperson to tell when the model is returning facts and when it is returning bullshit. |
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– so unless you are a subject matter expert (biology, medicine, chemistry, pharmacy, etc) I appeal to your sense of responsibility and ask you: |
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**to use the model only for testing, exploration, and just-for-fun. In no case should the answers of this model lead to implications that affect your health.** |
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--- |
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Here is what the autor/s write in the original model [card](https://huggingface.co/chaoyi-wu/PMC_LLAMA_7B_10_epoch/blob/main/README.md): |
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``` |
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This repo contains the latest version of PMC_LLaMA_7B, which is LLaMA-7b finetuned on the PMC papers in the S2ORC dataset. |
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Notably, different from chaoyi-wu/PMC_LLAMA_7B, this model is further trained for 10 epochs. |
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The model was trained with the following hyperparameters: |
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Epochs: 10 |
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Batch size: 128 |
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Cutoff length: 512 |
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Learning rate: 2e-5 |
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Each epoch we sample 512 tokens per paper for training. |
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``` |
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--- |
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### That's it! |
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If you have any further questions, feel free to contact me or start a discussion |
