Update README.md

README.md

@@ -1,3 +1,86 @@
 ---
-license: odc-by
+datasets:
+- S2ORC
+language:
+- en
+tags:
+- llama
+- ggml
+- pubmed
+- medicine
+- research
+- papers
 ---
+
+# ---
+
+
+---
+
+
+
+# PMC_LLaMA - finetuned on PubMed Central papers
+
+
+**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.**
+
+**It is a LLaMA model which is finetuned on PubMed Central papers from**
+**The Semantic Scholar Open Research Coprus [dataset](https://github.com/allenai/s2orc).**
+
+Currently I have only converted it into **new k-quant method Q5_K_M**. I will gladly make more versions on request.
+
+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
+
+A f-16 version could be found here: [nikuya3/alpaca-lora-7b-german-base-51k-ggml](https://huggingface.co/nikuya3/alpaca-lora-7b-german-base-51k-ggml)
+
+Compatible with **llama.cpp**, but also with:
+
+- **text-generation-webui**
+- **KoboldCpp**
+- **ParisNeo/GPT4All-UI**
+- **llama-cpp-python**
+- **ctransformers**
+
+
+---
+
+
+# CAVE!
+
+Being a professional myself and having tested the model, I can strongly advise that this model is best left in the hands of professionals.
+
+This model can produce very detailed and elaborate responses, but it tends to confabulate quite often in my opinion (considering the field of use).
+
+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.
+
+– so unless you are a subject matter expert (biology, medicine, chemistry, pharmacy, etc) I appeal to your sense of responsibility and ask you:
+
+**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.**
+
+
+---
+
+
+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):
+
+```
+This repo contains the latest version of PMC_LLaMA_7B, which is LLaMA-7b finetuned on the PMC papers in the S2ORC dataset.
+
+Notably, different from chaoyi-wu/PMC_LLAMA_7B, this model is further trained for 10 epochs.
+
+The model was trained with the following hyperparameters:
+
+Epochs: 10
+Batch size: 128
+Cutoff length: 512
+Learning rate: 2e-5
+Each epoch we sample 512 tokens per paper for training.
+```
+
+---
+
+
+### That's it!
+
+
+If you have any further questions, feel free to contact me or start a discussion