Locutusque_-_gpt2-large-medical-gguf

RichardErkhov/Locutusque_-_gpt2-large-medical-gguf

Locutusque_-_gpt2-large-medical-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

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.gguf11.7 GB · 100%

At a glance

Access: Public
Created: Oct 4, 2024
Updated: Oct 4, 2024
SHA: 8d1ce071

Model card

From the Hugging Face model README

Quantization made by Richard Erkhov.

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gpt2-large-medical - GGUF

Model creator: https://huggingface.co/Locutusque/
Original model: https://huggingface.co/Locutusque/gpt2-large-medical/

Name	Quant method	Size
gpt2-large-medical.Q2_K.gguf	Q2_K	0.32GB
gpt2-large-medical.IQ3_XS.gguf	IQ3_XS	0.36GB
gpt2-large-medical.IQ3_S.gguf	IQ3_S	0.37GB
gpt2-large-medical.Q3_K_S.gguf	Q3_K_S	0.37GB
gpt2-large-medical.IQ3_M.gguf	IQ3_M	0.4GB
gpt2-large-medical.Q3_K.gguf	Q3_K	0.43GB
gpt2-large-medical.Q3_K_M.gguf	Q3_K_M	0.43GB
gpt2-large-medical.Q3_K_L.gguf	Q3_K_L	0.46GB
gpt2-large-medical.IQ4_XS.gguf	IQ4_XS	0.44GB
gpt2-large-medical.Q4_0.gguf	Q4_0	0.46GB
gpt2-large-medical.IQ4_NL.gguf	IQ4_NL	0.47GB
gpt2-large-medical.Q4_K_S.gguf	Q4_K_S	0.47GB
gpt2-large-medical.Q4_K.gguf	Q4_K	0.51GB
gpt2-large-medical.Q4_K_M.gguf	Q4_K_M	0.51GB
gpt2-large-medical.Q4_1.gguf	Q4_1	0.51GB
gpt2-large-medical.Q5_0.gguf	Q5_0	0.55GB
gpt2-large-medical.Q5_K_S.gguf	Q5_K_S	0.55GB
gpt2-large-medical.Q5_K.gguf	Q5_K	0.59GB
gpt2-large-medical.Q5_K_M.gguf	Q5_K_M	0.59GB
gpt2-large-medical.Q5_1.gguf	Q5_1	0.6GB
gpt2-large-medical.Q6_K.gguf	Q6_K	0.65GB
gpt2-large-medical.Q8_0.gguf	Q8_0	0.84GB

Original model description:

datasets:

BI55/MedText
pubmed_qa metrics:
bleu
perplexity pipeline_tag: text-generation widget:
text: <|USER|> What is Schizophrenia? <|ASSISTANT|> inference: parameters: temperature: 0.8 top_p: 0.14 top_k: 41 max_new_tokens: 15 repetition_penalty: 1.176 tags:
medical

A further fine-tuned version of Locutusque/gpt2-large-conversational on MedText and pubmed_qa

Evaluation

This model was evaluated using GPT-3.5, and it was asked medical questions. It achieved an average accuracy of 80%.

Created: Oct 4, 2024
Updated: Oct 4, 2024