ehartford

# This supports merging as many adapters as you want.

# python merge_adapters.py --base_model_name_or_path <base_model> --peft_model_paths <adapter1> <adapter2> <adapter3> --output_dir <merged_model>

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
import os
import argparse

padeoe

🤗Huggingface Model Downloader

Considering the lack of multi-threaded download support in the official huggingface-cli, and the inadequate error handling in hf_transfer, this command-line tool smartly utilizes wget or aria2 for LFS files and git clone for the rest.

Features

• ⏯️ Resume from breakpoint: You can re-run it or Ctrl+C anytime.
• 🚀 Multi-threaded Download: Utilize multiple threads to speed up the download process.
• 🚫 File Exclusion: Use --exclude or --include to skip or specify files, save time for models with duplicate formats (e.g., *.bin or *.safetensors).
• 🔐 Auth Support: For gated models that require Huggingface login, use --hf_username and --hf_token to authenticate.
• 🪞 Mirror Site Support: Set up with HF_ENDPOINT environment variable.

mlabonne

# Example usage:
# python merge_peft.py --base_model=meta-llama/Llama-2-7b-hf --peft_model=./qlora-out --hub_id=alpaca-qlora

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

import argparse

def get_args():

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models