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91 lines
4.7 KiB
91 lines
4.7 KiB
'''
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RunPod | Transformer | Model Fetcher
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'''
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import argparse
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import torch
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from transformers import (GPTNeoForCausalLM, GPT2Tokenizer, GPTNeoXForCausalLM,
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GPTNeoXTokenizerFast, GPTJForCausalLM, AutoTokenizer, AutoModelForCausalLM)
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from huggingface_hub import snapshot_download, hf_hub_download
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def download_model(model_name):
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# --------------------------------- Neo 1.3B --------------------------------- #
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if model_name == 'gpt-neo-1.3B':
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GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
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GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
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# --------------------------------- Neo 2.7B --------------------------------- #
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elif model_name == 'gpt-neo-2.7B':
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GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-2.7B", torch_dtype=torch.float16)
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GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
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# ----------------------------------- NeoX ----------------------------------- #
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elif model_name == 'gpt-neox-20b':
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GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b").half()
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GPTNeoXTokenizerFast.from_pretrained("EleutherAI/gpt-neox-20b")
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# --------------------------------- Pygmalion -------------------------------- #
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elif model_name == 'pygmalion-6b':
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# AutoModelForCausalLM.from_pretrained("PygmalionAI/pygmalion-6b", load_in_8bit=True)
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# AutoTokenizer.from_pretrained("PygmalionAI/pygmalion-6b")
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snapshot_download(repo_id="PygmalionAI/pygmalion-6b", revision="main")
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# --------------------------------- Pygmalion -------------------------------- #
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elif model_name == 'pygmalion-6b-4bit-128g':
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snapshot_download(repo_id="mayaeary/pygmalion-6b-4bit-128g", revision="main")
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# --------------------------------- Pygmalion -------------------------------- #
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elif model_name == 'pygmalion-6b-gptq-4bit':
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# AutoModelForCausalLM.from_pretrained("OccamRazor/pygmalion-6b-gptq-4bit", from_pt=True)
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# AutoTokenizer.from_pretrained("OccamRazor/pygmalion-6b-gptq-4bit")
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snapshot_download(repo_id="OccamRazor/pygmalion-6b-gptq-4bit", revision="main")
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# ----------------------------------- GPT-J ----------------------------------- #
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elif model_name == 'gpt-j-6b':
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GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16",
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torch_dtype=torch.float16)
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AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
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# ------------------------------ PPO Shygmalion 6B ----------------------------- #
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elif model_name == 'ppo-shygmalion-6b':
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AutoModelForCausalLM.from_pretrained("TehVenom/PPO_Shygmalion-6b", load_in_8bit=True)
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AutoTokenizer.from_pretrained("TehVenom/PPO_Shygmalion-6b")
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# ------------------------------ Dolly Shygmalion 6B ----------------------------- #
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elif model_name == 'dolly-shygmalion-6b':
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AutoModelForCausalLM.from_pretrained("TehVenom/Dolly_Shygmalion-6b", load_in_8bit=True)
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AutoTokenizer.from_pretrained("TehVenom/Dolly_Shygmalion-6b")
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# ------------------------------ Erebus 13B (NSFW) ----------------------------- #
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elif model_name == 'erebus-13b':
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AutoModelForCausalLM.from_pretrained("KoboldAI/OPT-13B-Erebus", load_in_8bit=True)
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AutoTokenizer.from_pretrained("KoboldAI/OPT-13B-Erebus")
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# --------------------------- Alpaca 13B (Quantized) -------------------------- #
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elif model_name == 'gpt4-x-alpaca-13b-native-4bit-128g':
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AutoModelForCausalLM.from_pretrained("anon8231489123/gpt4-x-alpaca-13b-native-4bit-128g")
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AutoTokenizer.from_pretrained("anon8231489123/gpt4-x-alpaca-13b-native-4bit-128g")
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# --------------------------------- Alpaca 13B -------------------------------- #
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elif model_name == 'gpt4-x-alpaca':
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AutoModelForCausalLM.from_pretrained("chavinlo/gpt4-x-alpaca", load_in_8bit=True)
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AutoTokenizer.from_pretrained("chavinlo/gpt4-x-alpaca")
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# --------------------------------- RWKV Raven 7B -------------------------------- #
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elif model_name == 'rwkv-4-raven-7b':
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hf_hub_download(repo_id="BlinkDL/rwkv-4-raven", filename="RWKV-4-Raven-7B-v7-EngAndMore-20230404-ctx4096.pth")
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#https://huggingface.co/yahma/RWKV-14b_quant/resolve/main/RWKV-4-Pile-14B-20230213-8019.pqth
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# ---------------------------------------------------------------------------- #
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# Parse Arguments #
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# ---------------------------------------------------------------------------- #
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--model_name", type=str,
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default="gpt-neo-1.3B", help="URL of the model to download.")
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if __name__ == "__main__":
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args = parser.parse_args()
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download_model(args.model_name)
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