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Building a private medical ChatGPT!

Maziyar Panahi maziyarpanahi

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Building a private medical ChatGPT!
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maziyarpanahi / miqu-upload-hf.py
Created February 9, 2024 22:54 — forked from 152334H/miqu-upload-hf.py
upload miqu ckpt to hf
from transformers import LlamaConfig as LC, LlamaForCausalLM as LLM, LlamaTokenizer as LT
from accelerate import init_empty_weights, load_checkpoint_and_dispatch
import torch
lt = LT.from_pretrained("NousResearch/Llama-2-7b-hf")
c = LC.from_pretrained("NousResearch/Llama-2-70b-hf")
c.max_position_embeddings = 32764
c.rope_theta = 1000000
with init_empty_weights(): m = LLM(c)
m = m.half().eval()
m.requires_grad_(False)
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maziyarpanahi / gguf-merge.sh
Created February 2, 2024 08:30 — forked from crasm/gguf-merge.sh
Shell script for merging TheBloke's .gguf-split model files
#!/bin/sh
log() {
format="$1"; shift
# shellcheck disable=SC2059
>&2 printf "$format\n" "$@"
}
usage() {
>&2 cat <<EOF
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maziyarpanahi / PVE-HP-ssacli-smart-storage-admin.md
Created March 31, 2023 09:26 — forked from mrpeardotnet/PVE-HP-ssacli-smart-storage-admin.md
HP Smart Storage Admin CLI (ssacli) installation and usage on Proxmox PVE (6.x)

HP Smart Storage Admin CLI (ssacli) installation and usage on Proxmox PVE (6.x)

Why use HP Smart Storage Admin CLI?

You can use ssacli (smart storage administrator command line interface) tool to manage any of supported HP Smart Array Controllers in your Proxmox host without need to reboot your server to access Smart Storage Administrator in BIOS. That means no host downtime when managing your storage.

CLI is not as convenient as GUI interface provided by BIOS or desktop utilities, but still allows you to fully manage your controller, physical disks and logical drives on the fly with no Proxmox host downtime.

ssacli replaces older hpssacli, but shares the same syntax and adds support for newer servers and controllers.

Installation

@maziyarpanahi
maziyarpanahi / readme.md
Created September 4, 2019 12:32 — forked from baraldilorenzo/readme.md
VGG-16 pre-trained model for Keras

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman