metadata

inference: false
license: other
tags:
  - medical
datasets:
  - allenai/s2orc

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Chaoyi Wi's PMC_LLAMA 7B GPTQ

These files are GPTQ 4bit model files for Chaoyi Wi's PMC_LLAMA 7B.

It is the result of quantising to 4bit using GPTQ-for-LLaMa.

Other repositories available

How to easily download and use this model in text-generation-webui

Downloading the model

Click the Model tab.
Under Download custom model or LoRA, enter TheBloke/PMC_LLAMA-7B-GPTQ.
Click Download.
Wait until it says it's finished downloading.
Untick "Autoload model"
Click the Refresh icon next to Model in the top left.

To use with AutoGPTQ (if installed)

In the Model drop-down: choose the model you just downloaded, PMC_LLAMA-7B-GPTQ.
Under GPTQ, tick AutoGPTQ.
Click Save settings for this model in the top right.
Click Reload the Model in the top right.
Once it says it's loaded, click the Text Generation tab and enter a prompt!

To use with GPTQ-for-LLaMa

In the Model drop-down: choose the model you just downloaded, PMC_LLAMA-7B-GPTQ.
If you see an error in the bottom right, ignore it - it's temporary.
Fill out the GPTQ parameters on the right: Bits = 4, Groupsize = 128, model_type = Llama
Click Save settings for this model in the top right.
Click Reload the Model in the top right.
Once it says it's loaded, click the Text Generation tab and enter a prompt!

Provided files

PMC_LLAMA-7B-GPTQ-4bit-128g.no-act.order.safetensors

This will work with all versions of GPTQ-for-LLaMa, and with AutoGPTQ.

It was created with

PMC_LLAMA-7B-GPTQ-4bit-128g.no-act.order.safetensors
- Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches
- Works with AutoGPTQ
- Works with text-generation-webui one-click-installers
- Parameters: Groupsize = 128. Act Order / desc_act = False.

Discord

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Thanks, and how to contribute.

Thanks to the chirper.ai team!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

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Patreon special mentions: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.

Thank you to all my generous patrons and donaters!

Original model card: Chaoyi Wi's PMC_LLAMA 7B

This repo contains PMC_LLaMA_7B, which is LLaMA-7b finetuned on the PMC papers in S2ORC dataset.

The model was trained with the following hyperparameters:

Epochs: 5
Batch size: 128
Cutoff length: 512
Learning rate: 2e-5

Each epoch we sample 512 tokens per paper for training.

The model can be loaded as following:

import transformers
import torch
tokenizer = transformers.LlamaTokenizer.from_pretrained('chaoyi-wu/PMC_LLAMA_7B')
model = transformers.LlamaForCausalLM.from_pretrained('chaoyi-wu/PMC_LLAMA_7B')
sentence = 'Hello, doctor'
batch = tokenizer(
            sentence,
            return_tensors="pt",
            add_special_tokens=False
        )
with torch.no_grad():
    generated = model.generate(inputs = batch["input_ids"], max_length=200, do_sample=True, top_k=50)
    print('model predict: ',tokenizer.decode(generated[0]))