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RichardErkhov/
Sharathhebbar24_-_Med_GPT2-gguf

Name: Sharathhebbar24_-_Med_GPT2-gguf
Availability: InStock

endpoints_compatible

gguf

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Quantization made by Richard Erkhov.

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Med_GPT2 - GGUF

Model creator: https://huggingface.co/Sharathhebbar24/
Original model: https://huggingface.co/Sharathhebbar24/Med_GPT2/

Name	Quant method	Size
Med_GPT2.Q2_K.gguf	Q2_K	0.08GB
Med_GPT2.IQ3_XS.gguf	IQ3_XS	0.08GB
Med_GPT2.IQ3_S.gguf	IQ3_S	0.08GB
Med_GPT2.Q3_K_S.gguf	Q3_K_S	0.08GB
Med_GPT2.IQ3_M.gguf	IQ3_M	0.09GB
Med_GPT2.Q3_K.gguf	Q3_K	0.09GB
Med_GPT2.Q3_K_M.gguf	Q3_K_M	0.09GB
Med_GPT2.Q3_K_L.gguf	Q3_K_L	0.1GB
Med_GPT2.IQ4_XS.gguf	IQ4_XS	0.1GB
Med_GPT2.Q4_0.gguf	Q4_0	0.1GB
Med_GPT2.IQ4_NL.gguf	IQ4_NL	0.1GB
Med_GPT2.Q4_K_S.gguf	Q4_K_S	0.1GB
Med_GPT2.Q4_K.gguf	Q4_K	0.11GB
Med_GPT2.Q4_K_M.gguf	Q4_K_M	0.11GB
Med_GPT2.Q4_1.gguf	Q4_1	0.11GB
Med_GPT2.Q5_0.gguf	Q5_0	0.11GB
Med_GPT2.Q5_K_S.gguf	Q5_K_S	0.11GB
Med_GPT2.Q5_K.gguf	Q5_K	0.12GB
Med_GPT2.Q5_K_M.gguf	Q5_K_M	0.12GB
Med_GPT2.Q5_1.gguf	Q5_1	0.12GB
Med_GPT2.Q6_K.gguf	Q6_K	0.13GB
Med_GPT2.Q8_0.gguf	Q8_0	0.17GB

Original model description:

license: apache-2.0 datasets:

gamino/wiki_medical_terms language:
en pipeline_tag: text-generation tags:
medical

This is a finetuned version of gamino/wiki_medical_terms

Model description

GPT-2 is a transformers model pre-trained on a very large corpus of English data in a self-supervised fashion. This means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences.

More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifting one token (word or piece of word) to the right. The model uses a masking mechanism to make sure the predictions for the token i only use the inputs from 1 to i but not the future tokens.

This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was trained for, however, which is generating texts from a prompt.

To use this model

python

>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> model_name = "Sharathhebbar24/chat_gpt2_dpo"
>>> model = AutoModelForCausalLM.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> def generate_text(prompt):
>>>  inputs = tokenizer.encode(prompt, return_tensors='pt')
>>>  outputs = model.generate(inputs, max_length=64, pad_token_id=tokenizer.eos_token_id)
>>>  generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
>>>  return generated[:generated.rfind(".")+1]
>>> prompt = "What is Paracetamol"
>>> res = generate_text(prompt)
>>> res

RichardErkhov/Sharathhebbar24_-_Med_GPT2-gguf

Original model description:

Model description

To use this model

RichardErkhov/
Sharathhebbar24_-_Med_GPT2-gguf