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loading_from_pretrained.py
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"""Loading Pretrained Models Utilities.
This module contains functions for loading pretrained models from the Hugging Face Hub.
"""
import dataclasses
import logging
import os
import re
from typing import Dict, Optional, Union, cast
import einops
import torch
from huggingface_hub import HfApi
from transformers import AutoConfig, AutoModelForCausalLM, BertForPreTraining
import transformer_lens.utils as utils
from transformer_lens.HookedTransformerConfig import HookedTransformerConfig
OFFICIAL_MODEL_NAMES = [
"gpt2",
"gpt2-medium",
"gpt2-large",
"gpt2-xl",
"distilgpt2",
"facebook/opt-125m",
"facebook/opt-1.3b",
"facebook/opt-2.7b",
"facebook/opt-6.7b",
"facebook/opt-13b",
"facebook/opt-30b",
"facebook/opt-66b",
"EleutherAI/gpt-neo-125M",
"EleutherAI/gpt-neo-1.3B",
"EleutherAI/gpt-neo-2.7B",
"EleutherAI/gpt-j-6B",
"EleutherAI/gpt-neox-20b",
"stanford-crfm/alias-gpt2-small-x21",
"stanford-crfm/battlestar-gpt2-small-x49",
"stanford-crfm/caprica-gpt2-small-x81",
"stanford-crfm/darkmatter-gpt2-small-x343",
"stanford-crfm/expanse-gpt2-small-x777",
"stanford-crfm/arwen-gpt2-medium-x21",
"stanford-crfm/beren-gpt2-medium-x49",
"stanford-crfm/celebrimbor-gpt2-medium-x81",
"stanford-crfm/durin-gpt2-medium-x343",
"stanford-crfm/eowyn-gpt2-medium-x777",
"EleutherAI/pythia-14m",
"EleutherAI/pythia-31m",
"EleutherAI/pythia-70m",
"EleutherAI/pythia-160m",
"EleutherAI/pythia-410m",
"EleutherAI/pythia-1b",
"EleutherAI/pythia-1.4b",
"EleutherAI/pythia-2.8b",
"EleutherAI/pythia-6.9b",
"EleutherAI/pythia-12b",
"EleutherAI/pythia-70m-deduped",
"EleutherAI/pythia-160m-deduped",
"EleutherAI/pythia-410m-deduped",
"EleutherAI/pythia-1b-deduped",
"EleutherAI/pythia-1.4b-deduped",
"EleutherAI/pythia-2.8b-deduped",
"EleutherAI/pythia-6.9b-deduped",
"EleutherAI/pythia-12b-deduped",
"EleutherAI/pythia-70m-v0",
"EleutherAI/pythia-160m-v0",
"EleutherAI/pythia-410m-v0",
"EleutherAI/pythia-1b-v0",
"EleutherAI/pythia-1.4b-v0",
"EleutherAI/pythia-2.8b-v0",
"EleutherAI/pythia-6.9b-v0",
"EleutherAI/pythia-12b-v0",
"EleutherAI/pythia-70m-deduped-v0",
"EleutherAI/pythia-160m-deduped-v0",
"EleutherAI/pythia-410m-deduped-v0",
"EleutherAI/pythia-1b-deduped-v0",
"EleutherAI/pythia-1.4b-deduped-v0",
"EleutherAI/pythia-2.8b-deduped-v0",
"EleutherAI/pythia-6.9b-deduped-v0",
"EleutherAI/pythia-12b-deduped-v0",
"EleutherAI/pythia-160m-seed1",
"EleutherAI/pythia-160m-seed2",
"EleutherAI/pythia-160m-seed3",
"NeelNanda/SoLU_1L_v9_old",
"NeelNanda/SoLU_2L_v10_old",
"NeelNanda/SoLU_4L_v11_old",
"NeelNanda/SoLU_6L_v13_old",
"NeelNanda/SoLU_8L_v21_old",
"NeelNanda/SoLU_10L_v22_old",
"NeelNanda/SoLU_12L_v23_old",
"NeelNanda/SoLU_1L512W_C4_Code",
"NeelNanda/SoLU_2L512W_C4_Code",
"NeelNanda/SoLU_3L512W_C4_Code",
"NeelNanda/SoLU_4L512W_C4_Code",
"NeelNanda/SoLU_6L768W_C4_Code",
"NeelNanda/SoLU_8L1024W_C4_Code",
"NeelNanda/SoLU_10L1280W_C4_Code",
"NeelNanda/SoLU_12L1536W_C4_Code",
"NeelNanda/GELU_1L512W_C4_Code",
"NeelNanda/GELU_2L512W_C4_Code",
"NeelNanda/GELU_3L512W_C4_Code",
"NeelNanda/GELU_4L512W_C4_Code",
"NeelNanda/Attn_Only_1L512W_C4_Code",
"NeelNanda/Attn_Only_2L512W_C4_Code",
"NeelNanda/Attn_Only_3L512W_C4_Code",
"NeelNanda/Attn_Only_4L512W_C4_Code",
"NeelNanda/Attn-Only-2L512W-Shortformer-6B-big-lr",
"NeelNanda/SoLU_1L512W_Wiki_Finetune",
"NeelNanda/SoLU_4L512W_Wiki_Finetune",
"ArthurConmy/redwood_attn_2l",
"llama-7b-hf",
"llama-13b-hf",
"llama-30b-hf",
"llama-65b-hf",
"meta-llama/Llama-2-7b-hf",
"meta-llama/Llama-2-7b-chat-hf",
"meta-llama/Llama-2-13b-hf",
"meta-llama/Llama-2-13b-chat-hf",
"meta-llama/Llama-2-70b-chat-hf",
"CodeLlama-7b-hf",
"CodeLlama-7b-Python-hf",
"CodeLlama-7b-Instruct-hf",
"meta-llama/Meta-Llama-3-8B",
"meta-llama/Meta-Llama-3-8B-Instruct",
"meta-llama/Meta-Llama-3-70B",
"meta-llama/Meta-Llama-3-70B-Instruct",
"Baidicoot/Othello-GPT-Transformer-Lens",
"bert-base-cased",
"roneneldan/TinyStories-1M",
"roneneldan/TinyStories-3M",
"roneneldan/TinyStories-8M",
"roneneldan/TinyStories-28M",
"roneneldan/TinyStories-33M",
"roneneldan/TinyStories-Instruct-1M",
"roneneldan/TinyStories-Instruct-3M",
"roneneldan/TinyStories-Instruct-8M",
"roneneldan/TinyStories-Instruct-28M",
"roneneldan/TinyStories-Instruct-33M",
"roneneldan/TinyStories-1Layer-21M",
"roneneldan/TinyStories-2Layers-33M",
"roneneldan/TinyStories-Instuct-1Layer-21M",
"roneneldan/TinyStories-Instruct-2Layers-33M",
"stabilityai/stablelm-base-alpha-3b",
"stabilityai/stablelm-base-alpha-7b",
"stabilityai/stablelm-tuned-alpha-3b",
"stabilityai/stablelm-tuned-alpha-7b",
"mistralai/Mistral-7B-v0.1",
"mistralai/Mistral-7B-Instruct-v0.1",
"mistralai/Mixtral-8x7B-v0.1",
"mistralai/Mixtral-8x7B-Instruct-v0.1",
"bigscience/bloom-560m",
"bigscience/bloom-1b1",
"bigscience/bloom-1b7",
"bigscience/bloom-3b",
"bigscience/bloom-7b1",
"bigcode/santacoder",
"Qwen/Qwen-1_8B",
"Qwen/Qwen-7B",
"Qwen/Qwen-14B",
"Qwen/Qwen-1_8B-Chat",
"Qwen/Qwen-7B-Chat",
"Qwen/Qwen-14B-Chat",
"Qwen/Qwen1.5-0.5B",
"Qwen/Qwen1.5-0.5B-Chat",
"Qwen/Qwen1.5-1.8B",
"Qwen/Qwen1.5-1.8B-Chat",
"Qwen/Qwen1.5-4B",
"Qwen/Qwen1.5-4B-Chat",
"Qwen/Qwen1.5-7B",
"Qwen/Qwen1.5-7B-Chat",
"Qwen/Qwen1.5-14B",
"Qwen/Qwen1.5-14B-Chat",
"microsoft/phi-1",
"microsoft/phi-1_5",
"microsoft/phi-2",
"google/gemma-2b",
"google/gemma-7b",
"google/gemma-2b-it",
"google/gemma-7b-it",
"01-ai/Yi-6B",
"01-ai/Yi-34B",
"01-ai/Yi-6B-Chat",
"01-ai/Yi-34B-Chat",
]
"""Official model names for models on HuggingFace."""
# Model Aliases:
MODEL_ALIASES = {
"NeelNanda/SoLU_1L_v9_old": ["solu-1l-pile", "solu-1l-old"],
"NeelNanda/SoLU_2L_v10_old": ["solu-2l-pile", "solu-2l-old"],
"NeelNanda/SoLU_4L_v11_old": ["solu-4l-pile", "solu-4l-old"],
"NeelNanda/SoLU_6L_v13_old": ["solu-6l-pile", "solu-6l-old"],
"NeelNanda/SoLU_8L_v21_old": ["solu-8l-pile", "solu-8l-old"],
"NeelNanda/SoLU_10L_v22_old": ["solu-10l-pile", "solu-10l-old"],
"NeelNanda/SoLU_12L_v23_old": ["solu-12l-pile", "solu-12l-old"],
"NeelNanda/SoLU_1L512W_C4_Code": ["solu-1l", "solu-1l-new", "solu-1l-c4-code"],
"NeelNanda/SoLU_2L512W_C4_Code": ["solu-2l", "solu-2l-new", "solu-2l-c4-code"],
"NeelNanda/SoLU_3L512W_C4_Code": ["solu-3l", "solu-3l-new", "solu-3l-c4-code"],
"NeelNanda/SoLU_4L512W_C4_Code": ["solu-4l", "solu-4l-new", "solu-4l-c4-code"],
"NeelNanda/GELU_1L512W_C4_Code": ["gelu-1l", "gelu-1l-new", "gelu-1l-c4-code"],
"NeelNanda/GELU_2L512W_C4_Code": ["gelu-2l", "gelu-2l-new", "gelu-2l-c4-code"],
"NeelNanda/GELU_3L512W_C4_Code": ["gelu-3l", "gelu-3l-new", "gelu-3l-c4-code"],
"NeelNanda/GELU_4L512W_C4_Code": ["gelu-4l", "gelu-4l-new", "gelu-4l-c4-code"],
"NeelNanda/Attn_Only_1L512W_C4_Code": [
"attn-only-1l",
"attn-only-1l-new",
"attn-only-1l-c4-code",
],
"NeelNanda/Attn_Only_2L512W_C4_Code": [
"attn-only-2l",
"attn-only-2l-new",
"attn-only-2l-c4-code",
],
"NeelNanda/Attn_Only_3L512W_C4_Code": [
"attn-only-3l",
"attn-only-3l-new",
"attn-only-3l-c4-code",
],
"NeelNanda/Attn_Only_4L512W_C4_Code": [
"attn-only-4l",
"attn-only-4l-new",
"attn-only-4l-c4-code",
],
"NeelNanda/SoLU_6L768W_C4_Code": ["solu-6l", "solu-6l-new", "solu-6l-c4-code"],
"NeelNanda/SoLU_8L1024W_C4_Code": ["solu-8l", "solu-8l-new", "solu-8l-c4-code"],
"NeelNanda/SoLU_10L1280W_C4_Code": ["solu-10l", "solu-10l-new", "solu-10l-c4-code"],
"NeelNanda/SoLU_12L1536W_C4_Code": ["solu-12l", "solu-12l-new", "solu-12l-c4-code"],
"NeelNanda/Attn-Only-2L512W-Shortformer-6B-big-lr": [
"attn-only-2l-demo",
"attn-only-2l-shortformer-6b-big-lr",
"attn-only-2l-induction-demo",
"attn-only-demo",
],
"NeelNanda/SoLU_1L512W_Wiki_Finetune": [
"solu-1l-wiki",
"solu-1l-wiki-finetune",
"solu-1l-finetune",
],
"NeelNanda/SoLU_4L512W_Wiki_Finetune": [
"solu-4l-wiki",
"solu-4l-wiki-finetune",
"solu-4l-finetune",
],
"EleutherAI/pythia-14m": [
"pythia-14m",
],
"EleutherAI/pythia-31m": [
"pythia-31m",
],
"EleutherAI/pythia-70m": [
"pythia-70m",
"pythia",
"EleutherAI/pythia-19m",
"pythia-19m", # EleutherAI renamed this model
],
"EleutherAI/pythia-160m": [
"pythia-160m",
"EleutherAI/pythia-125m",
"pythia-125m", # EleutherAI renamed this model"
],
"EleutherAI/pythia-410m": [
"pythia-410m",
"EleutherAI/pythia-350m",
"pythia-350m", # EleutherAI renamed this model
],
"EleutherAI/pythia-1b": [
"pythia-1b",
"EleutherAI/pythia-800m",
"pythia-800m", # EleutherAI renamed this model
],
"EleutherAI/pythia-1.4b": [
"pythia-1.4b",
"EleutherAI/pythia-1.3b",
"pythia-1.3b", # EleutherAI renamed this model
],
"EleutherAI/pythia-2.8b": [
"pythia-2.8b",
"EleutherAI/pythia-2.7b",
"pythia-2.7b", # EleutherAI renamed this model
],
"EleutherAI/pythia-6.9b": [
"pythia-6.9b",
"EleutherAI/pythia-6.7b",
"pythia-6.7b", # EleutherAI renamed this model
],
"EleutherAI/pythia-12b": [
"pythia-12b",
"EleutherAI/pythia-13b",
"pythia-13b", # EleutherAI renamed this model
],
"EleutherAI/pythia-70m-deduped": [
"pythia-70m-deduped",
"EleutherAI/pythia-19m-deduped", # EleutherAI renamed this model
"pythia-19m-deduped",
],
"EleutherAI/pythia-160m-deduped": [
"pythia-160m-deduped",
"EleutherAI/pythia-125m-deduped", # EleutherAI renamed this model
"pythia-125m-deduped",
],
"EleutherAI/pythia-410m-deduped": [
"pythia-410m-deduped",
"EleutherAI/pythia-350m-deduped", # EleutherAI renamed this model
"pythia-350m-deduped",
],
"EleutherAI/pythia-1b-deduped": [
"pythia-1b-deduped",
"EleutherAI/pythia-800m-deduped", # EleutherAI renamed this model
"pythia-800m-deduped",
],
"EleutherAI/pythia-1.4b-deduped": [
"pythia-1.4b-deduped",
"EleutherAI/pythia-1.3b-deduped", # EleutherAI renamed this model
"pythia-1.3b-deduped",
],
"EleutherAI/pythia-2.8b-deduped": [
"pythia-2.8b-deduped",
"EleutherAI/pythia-2.7b-deduped", # EleutherAI renamed this model
"pythia-2.7b-deduped",
],
"EleutherAI/pythia-6.9b-deduped": [
"pythia-6.9b-deduped",
"EleutherAI/pythia-6.7b-deduped", # EleutherAI renamed this model
"pythia-6.7b-deduped",
],
"EleutherAI/pythia-12b-deduped": [
"pythia-12b-deduped",
"EleutherAI/pythia-13b-deduped", # EleutherAI renamed this model
"pythia-13b-deduped",
],
"EleutherAI/pythia-70m-v0": [
"pythia-70m-v0",
"pythia-v0",
"EleutherAI/pythia-19m-v0",
"pythia-19m-v0", # EleutherAI renamed this model
],
"EleutherAI/pythia-160m-v0": [
"pythia-160m-v0",
"EleutherAI/pythia-125m-v0",
"pythia-125m-v0", # EleutherAI renamed this model"
],
"EleutherAI/pythia-410m-v0": [
"pythia-410m-v0",
"EleutherAI/pythia-350m-v0",
"pythia-350m-v0", # EleutherAI renamed this model
],
"EleutherAI/pythia-1b-v0": [
"pythia-1b-v0",
"EleutherAI/pythia-800m-v0",
"pythia-800m-v0", # EleutherAI renamed this model
],
"EleutherAI/pythia-1.4b-v0": [
"pythia-1.4b-v0",
"EleutherAI/pythia-1.3b-v0",
"pythia-1.3b-v0", # EleutherAI renamed this model
],
"EleutherAI/pythia-2.8b-v0": [
"pythia-2.8b-v0",
"EleutherAI/pythia-2.7b-v0",
"pythia-2.7b-v0", # EleutherAI renamed this model
],
"EleutherAI/pythia-6.9b-v0": [
"pythia-6.9b-v0",
"EleutherAI/pythia-6.7b-v0",
"pythia-6.7b-v0", # EleutherAI renamed this model
],
"EleutherAI/pythia-12b-v0": [
"pythia-12b-v0",
"EleutherAI/pythia-13b-v0",
"pythia-13b-v0", # EleutherAI renamed this model
],
"EleutherAI/pythia-70m-deduped-v0": [
"pythia-70m-deduped-v0",
"EleutherAI/pythia-19m-deduped-v0", # EleutherAI renamed this model
"pythia-19m-deduped-v0",
],
"EleutherAI/pythia-160m-deduped-v0": [
"pythia-160m-deduped-v0",
"EleutherAI/pythia-125m-deduped-v0", # EleutherAI renamed this model
"pythia-125m-deduped-v0",
],
"EleutherAI/pythia-410m-deduped-v0": [
"pythia-410m-deduped-v0",
"EleutherAI/pythia-350m-deduped-v0", # EleutherAI renamed this model
"pythia-350m-deduped-v0",
],
"EleutherAI/pythia-1b-deduped-v0": [
"pythia-1b-deduped-v0",
"EleutherAI/pythia-800m-deduped-v0", # EleutherAI renamed this model
"pythia-800m-deduped-v0",
],
"EleutherAI/pythia-1.4b-deduped-v0": [
"pythia-1.4b-deduped-v0",
"EleutherAI/pythia-1.3b-deduped-v0", # EleutherAI renamed this model
"pythia-1.3b-deduped-v0",
],
"EleutherAI/pythia-2.8b-deduped-v0": [
"pythia-2.8b-deduped-v0",
"EleutherAI/pythia-2.7b-deduped-v0", # EleutherAI renamed this model
"pythia-2.7b-deduped-v0",
],
"EleutherAI/pythia-6.9b-deduped-v0": [
"pythia-6.9b-deduped-v0",
"EleutherAI/pythia-6.7b-deduped-v0", # EleutherAI renamed this model
"pythia-6.7b-deduped-v0",
],
"EleutherAI/pythia-12b-deduped-v0": [
"pythia-12b-deduped-v0",
"EleutherAI/pythia-13b-deduped-v0", # EleutherAI renamed this model
"pythia-13b-deduped-v0",
],
"EleutherAI/pythia-160m-seed1": [
"pythia-160m-seed1",
"EleutherAI/pythia-125m-seed1",
"pythia-125m-seed1", # EleutherAI renamed this model"
],
"EleutherAI/pythia-160m-seed2": [
"pythia-160m-seed2",
"EleutherAI/pythia-125m-seed2",
"pythia-125m-seed2", # EleutherAI renamed this model"
],
"EleutherAI/pythia-160m-seed3": [
"pythia-160m-seed3",
"EleutherAI/pythia-125m-seed3",
"pythia-125m-seed3", # EleutherAI renamed this model"
],
"gpt2": ["gpt2-small"],
"distilgpt2": ["distillgpt2", "distill-gpt2", "distil-gpt2", "gpt2-xs"],
"facebook/opt-125m": ["opt-125m", "opt-small", "opt"],
"facebook/opt-1.3b": ["opt-1.3b", "opt-medium"],
"facebook/opt-2.7b": ["opt-2.7b", "opt-large"],
"facebook/opt-6.7b": ["opt-6.7b", "opt-xl"],
"facebook/opt-13b": ["opt-13b", "opt-xxl"],
"facebook/opt-30b": ["opt-30b", "opt-xxxl"],
"facebook/opt-66b": ["opt-66b", "opt-xxxxl"],
"EleutherAI/gpt-neo-125M": ["gpt-neo-125M", "gpt-neo-small", "neo-small", "neo"],
"EleutherAI/gpt-neo-1.3B": ["gpt-neo-1.3B", "gpt-neo-medium", "neo-medium"],
"EleutherAI/gpt-neo-2.7B": ["gpt-neo-2.7B", "gpt-neo-large", "neo-large"],
"EleutherAI/gpt-j-6B": ["gpt-j-6B", "gpt-j", "gptj"],
"EleutherAI/gpt-neox-20b": ["gpt-neox-20b", "gpt-neox", "neox"],
"stanford-crfm/alias-gpt2-small-x21": [
"stanford-gpt2-small-a",
"alias-gpt2-small-x21",
"gpt2-mistral-small-a",
"gpt2-stanford-small-a",
],
"stanford-crfm/battlestar-gpt2-small-x49": [
"stanford-gpt2-small-b",
"battlestar-gpt2-small-x49",
"gpt2-mistral-small-b",
"gpt2-mistral-small-b",
],
"stanford-crfm/caprica-gpt2-small-x81": [
"stanford-gpt2-small-c",
"caprica-gpt2-small-x81",
"gpt2-mistral-small-c",
"gpt2-stanford-small-c",
],
"stanford-crfm/darkmatter-gpt2-small-x343": [
"stanford-gpt2-small-d",
"darkmatter-gpt2-small-x343",
"gpt2-mistral-small-d",
"gpt2-mistral-small-d",
],
"stanford-crfm/expanse-gpt2-small-x777": [
"stanford-gpt2-small-e",
"expanse-gpt2-small-x777",
"gpt2-mistral-small-e",
"gpt2-mistral-small-e",
],
"stanford-crfm/arwen-gpt2-medium-x21": [
"stanford-gpt2-medium-a",
"arwen-gpt2-medium-x21",
"gpt2-medium-small-a",
"gpt2-stanford-medium-a",
],
"stanford-crfm/beren-gpt2-medium-x49": [
"stanford-gpt2-medium-b",
"beren-gpt2-medium-x49",
"gpt2-medium-small-b",
"gpt2-stanford-medium-b",
],
"stanford-crfm/celebrimbor-gpt2-medium-x81": [
"stanford-gpt2-medium-c",
"celebrimbor-gpt2-medium-x81",
"gpt2-medium-small-c",
"gpt2-medium-small-c",
],
"stanford-crfm/durin-gpt2-medium-x343": [
"stanford-gpt2-medium-d",
"durin-gpt2-medium-x343",
"gpt2-medium-small-d",
"gpt2-stanford-medium-d",
],
"stanford-crfm/eowyn-gpt2-medium-x777": [
"stanford-gpt2-medium-e",
"eowyn-gpt2-medium-x777",
"gpt2-medium-small-e",
"gpt2-stanford-medium-e",
],
"ArthurConmy/redwood_attn_2l": ["redwood_attn_2l"],
"llama-7b-hf": ["llama-7b"],
"llama-13b-hf": ["llama-13b"],
"llama-30b-hf": ["llama-30b"],
"llama-65b-hf": ["llama-65b"],
"meta-llama/Llama-2-7b-hf": ["Llama-2-7b", "meta-llama/Llama-2-7b-hf"],
"meta-llama/Llama-2-7b-chat-hf": [
"Llama-2-7b-chat",
"meta-llama/Llama-2-7b-chat-hf",
],
"meta-llama/Llama-2-13b-hf": ["Llama-2-13b", "meta-llama/Llama-2-13b-hf"],
"meta-llama/Llama-2-13b-chat-hf": [
"Llama-2-13b-chat",
"meta-llama/Llama-2-13b-chat-hf",
],
"meta-llama/Llama-2-70b-chat-hf": ["Llama-2-70b-chat", "meta-llama-2-70b-chat-hf"],
"CodeLlama-7b-hf": ["CodeLlamallama-2-7b", "codellama/CodeLlama-7b-hf"],
"CodeLlama-7b-Python-hf": [
"CodeLlama-7b-python",
"codellama/CodeLlama-7b-Python-hf",
],
"CodeLlama-7b-Instruct-hf": [
"CodeLlama-7b-instruct",
"codellama/CodeLlama-7b-Instruct-hf",
],
"Baidicoot/Othello-GPT-Transformer-Lens": ["othello-gpt"],
"roneneldan/TinyStories-1M": ["tiny-stories-1M"],
"roneneldan/TinyStories-3M": ["tiny-stories-3M"],
"roneneldan/TinyStories-8M": ["tiny-stories-8M"],
"roneneldan/TinyStories-28M": ["tiny-stories-28M"],
"roneneldan/TinyStories-33M": ["tiny-stories-33M"],
"roneneldan/TinyStories-Instruct-1M": ["tiny-stories-instruct-1M"],
"roneneldan/TinyStories-Instruct-3M": ["tiny-stories-instruct-3M"],
"roneneldan/TinyStories-Instruct-8M": ["tiny-stories-instruct-8M"],
"roneneldan/TinyStories-Instruct-28M": ["tiny-stories-instruct-28M"],
"roneneldan/TinyStories-Instruct-33M": ["tiny-stories-instruct-33M"],
"roneneldan/TinyStories-1Layer-21M": ["tiny-stories-1L-21M"],
"roneneldan/TinyStories-2Layers-33M": ["tiny-stories-2L-33M"],
"roneneldan/TinyStories-Instuct-1Layer-21M": ["tiny-stories-instruct-1L-21M"],
"roneneldan/TinyStories-Instruct-2Layers-33M": ["tiny-stories-instruct-2L-33M"],
"stabilityai/stablelm-base-alpha-3b": [
"stablelm-base-alpha-3b",
"stablelm-base-3b",
],
"stabilityai/stablelm-base-alpha-7b": [
"stablelm-base-alpha-7b",
"stablelm-base-7b",
],
"stabilityai/stablelm-tuned-alpha-3b": [
"stablelm-tuned-alpha-3b",
"stablelm-tuned-3b",
],
"stabilityai/stablelm-tuned-alpha-7b": [
"stablelm-tuned-alpha-7b",
"stablelm-tuned-7b",
],
"mistralai/Mistral-7B-v0.1": ["mistral-7b"],
"mistralai/Mistral-7B-Instruct-v0.1": ["mistral-7b-instruct"],
"mistralai/Mixtral-8x7B-v0.1": ["mixtral", "mixtral-8x7b"],
"mistralai/Mixtral-8x7B-Instruct-v0.1": [
"mixtral-instruct",
"mixtral-8x7b-instruct",
],
"bigscience/bloom-560m": ["bloom-560m"],
"bigscience/bloom-1b1": ["bloom-1b1"],
"bigscience/bloom-1b7": ["bloom-1b7"],
"bigscience/bloom-3b": ["bloom-3b"],
"bigscience/bloom-7b1": ["bloom-7b1"],
"bigcode/santacoder": ["santacoder"],
"Qwen/Qwen-1_8B": ["qwen-1.8b"],
"Qwen/Qwen-7B": ["qwen-7b"],
"Qwen/Qwen-14B": ["qwen-14b"],
"Qwen/Qwen-1_8B-Chat": ["qwen-1.8b-chat"],
"Qwen/Qwen-7B-Chat": ["qwen-7b-chat"],
"Qwen/Qwen-14B-Chat": ["qwen-14b-chat"],
"Qwen/Qwen1.5-0.5B": ["qwen1.5-0.5b"],
"Qwen/Qwen1.5-0.5B-Chat": ["qwen1.5-0.5b-chat"],
"Qwen/Qwen1.5-1.8B": ["qwen1.5-1.8b"],
"Qwen/Qwen1.5-1.8B-Chat": ["qwen1.5-1.8b-chat"],
"Qwen/Qwen1.5-4B": ["qwen1.5-4b"],
"Qwen/Qwen1.5-4B-Chat": ["qwen1.5-4b-chat"],
"Qwen/Qwen1.5-7B": ["qwen1.5-7b"],
"Qwen/Qwen1.5-7B-Chat": ["qwen1.5-7b-chat"],
"Qwen/Qwen1.5-14B": ["qwen1.5-14b"],
"Qwen/Qwen1.5-14B-Chat": ["qwen1.5-14b-chat"],
"microsoft/phi-1": ["phi-1"],
"microsoft/phi-1_5": ["phi-1_5"],
"microsoft/phi-2": ["phi-2"],
"google/gemma-2b": ["gemma-2b"],
"google/gemma-7b": ["gemma-7b"],
"google/gemma-2b-it": ["gemma-2b-it"],
"google/gemma-7b-it": ["gemma-7b-it"],
"01-ai/Yi-6B": ["yi-6b", "Yi-6B"],
"01-ai/Yi-34B": ["yi-34b", "Yi-34B"],
"01-ai/Yi-6B-Chat": ["yi-6b-chat", "Yi-6B-Chat"],
"01-ai/Yi-34B-Chat": ["yi-34b-chat", "Yi-34B-Chat"],
}
"""Model aliases for models on HuggingFace."""
NON_HF_HOSTED_MODEL_NAMES = [
"llama-7b-hf",
"llama-13b-hf",
"llama-30b-hf",
"llama-65b-hf",
]
"""Official model names for models not hosted on HuggingFace."""
# Sets a default model alias, by convention the first one in the model alias table, else the official name if it has no aliases
DEFAULT_MODEL_ALIASES = [
MODEL_ALIASES[name][0] if name in MODEL_ALIASES else name for name in OFFICIAL_MODEL_NAMES
]
NEED_REMOTE_CODE_MODELS = (
"bigcode/santacoder",
"Qwen/Qwen-",
"microsoft/phi-2",
)
def make_model_alias_map():
"""
Converts OFFICIAL_MODEL_NAMES (the list of actual model names on
HuggingFace) and MODEL_ALIASES (a dictionary mapping official model names to
aliases) into a dictionary mapping all aliases to the official model name.
"""
model_alias_map = {}
for official_model_name in OFFICIAL_MODEL_NAMES:
aliases = MODEL_ALIASES.get(official_model_name, [])
for alias in aliases:
model_alias_map[alias.lower()] = official_model_name
model_alias_map[official_model_name.lower()] = official_model_name
return model_alias_map
def get_official_model_name(model_name: str):
"""
Returns the official model name for a given model name (or alias).
"""
model_alias_map = make_model_alias_map()
official_model_name = model_alias_map.get(model_name.lower(), None)
if official_model_name is None:
raise ValueError(
f"{model_name} not found. Valid official model names (excl aliases): {OFFICIAL_MODEL_NAMES}"
)
return official_model_name
def convert_hf_model_config(model_name: str, **kwargs):
"""
Returns the model config for a HuggingFace model, converted to a dictionary
in the HookedTransformerConfig format.
Takes the official_model_name as an input.
"""
# In case the user passed in an alias
official_model_name = get_official_model_name(model_name)
# Load HuggingFace model config
if "llama" in official_model_name.lower():
architecture = "LlamaForCausalLM"
elif "gemma" in official_model_name.lower():
architecture = "GemmaForCausalLM"
else:
huggingface_token = os.environ.get("HF_TOKEN", None)
hf_config = AutoConfig.from_pretrained(
official_model_name,
token=huggingface_token,
**kwargs,
)
architecture = hf_config.architectures[0]
if official_model_name.startswith(
("llama-7b", "meta-llama/Llama-2-7b")
): # same architecture for LLaMA and Llama-2
cfg_dict = {
"d_model": 4096,
"d_head": 4096 // 32,
"n_heads": 32,
"d_mlp": 11008,
"n_layers": 32,
"n_ctx": 2048 if official_model_name.startswith("llama-7b") else 4096,
"eps": 1e-6 if official_model_name.startswith("llama-7b") else 1e-5,
"d_vocab": 32000,
"act_fn": "silu",
"normalization_type": "RMS",
"positional_embedding_type": "rotary",
"rotary_adjacent_pairs": False,
"rotary_dim": 4096 // 32,
"final_rms": True,
"gated_mlp": True,
}
elif official_model_name.startswith("CodeLlama-7b"): # same architecture CodeLlama and Llama-2
cfg_dict = {
"d_model": 4096,
"d_head": 4096 // 32,
"n_heads": 32,
"d_mlp": 11008,
"n_layers": 32,
"n_ctx": 4096,
"eps": 1e-5,
"d_vocab": 32016,
"act_fn": "silu",
"normalization_type": "RMS",
"positional_embedding_type": "rotary",
"rotary_dim": 4096 // 32,
"final_rms": True,
"gated_mlp": True,
"rotary_base": 1000000,
}
if "python" in official_model_name.lower():
# The vocab size of python version of CodeLlama-7b is 32000
cfg_dict["d_vocab"] = 32000
elif official_model_name.startswith(
("llama-13b", "meta-llama/Llama-2-13b")
): # same architecture for LLaMA and Llama-2
cfg_dict = {
"d_model": 5120,
"d_head": 5120 // 40,
"n_heads": 40,
"d_mlp": 13824,
"n_layers": 40,
"n_ctx": 2048 if official_model_name.startswith("llama-13b") else 4096,
"eps": 1e-6 if official_model_name.startswith("llama-13b") else 1e-5,
"d_vocab": 32000,
"act_fn": "silu",
"normalization_type": "RMS",
"positional_embedding_type": "rotary",
"rotary_adjacent_pairs": False,
"rotary_dim": 5120 // 40,
"final_rms": True,
"gated_mlp": True,
}
elif "llama-30b" in official_model_name:
cfg_dict = {
"d_model": 6656,
"d_head": 6656 // 52,
"n_heads": 52,
"d_mlp": 17920,
"n_layers": 60,
"n_ctx": 2048,
"eps": 1e-6,
"d_vocab": 32000,
"act_fn": "silu",
"normalization_type": "RMS",
"positional_embedding_type": "rotary",
"rotary_adjacent_pairs": False,
"rotary_dim": 6656 // 52,
"final_rms": True,
"gated_mlp": True,
}
elif "llama-65b" in official_model_name:
cfg_dict = {
"d_model": 8192,
"d_head": 8192 // 64,
"n_heads": 64,
"d_mlp": 22016,
"n_layers": 80,
"n_ctx": 2048,
"eps": 1e-6,
"d_vocab": 32000,
"act_fn": "silu",
"normalization_type": "RMS",
"positional_embedding_type": "rotary",
"rotary_dim": 8192 // 64,
"rotary_adjacent_pairs": False,
"final_rms": True,
"gated_mlp": True,
}
elif "Llama-2-70b" in official_model_name:
cfg_dict = {
"d_model": 8192,
"d_head": 128,
"n_heads": 64,
"d_mlp": 28672,
"n_layers": 80,
"n_ctx": 4096,
"eps": 1e-5,
"d_vocab": 32000,
"act_fn": "silu",
"n_key_value_heads": 8,
"normalization_type": "RMS",
"positional_embedding_type": "rotary",
"rotary_adjacent_pairs": False,
"rotary_dim": 128,
"final_rms": True,
"gated_mlp": True,
}
elif "Meta-Llama-3-8B" in official_model_name:
cfg_dict = {
"d_model": 4096,
"d_head": 128,
"n_heads": 32,
"d_mlp": 14336,
"n_layers": 32,
"n_ctx": 8192,
"eps": 1e-5,
"d_vocab": 128256,
"act_fn": "silu",
"n_key_value_heads": 8,
"normalization_type": "RMS",
"positional_embedding_type": "rotary",
"rotary_adjacent_pairs": False,
"rotary_dim": 128,
"final_rms": True,
"gated_mlp": True,
}
elif "Meta-Llama-3-70B" in official_model_name:
cfg_dict = {
"d_model": 8192,
"d_head": 128,
"n_heads": 64,
"d_mlp": 28672,
"n_layers": 80,
"n_ctx": 8192,
"eps": 1e-5,
"d_vocab": 128256,
"act_fn": "silu",
"n_key_value_heads": 8,
"normalization_type": "RMS",
"positional_embedding_type": "rotary",
"rotary_adjacent_pairs": False,
"rotary_dim": 128,
"final_rms": True,
"gated_mlp": True,
}
elif architecture == "GPTNeoForCausalLM":
cfg_dict = {
"d_model": hf_config.hidden_size,
"d_head": hf_config.hidden_size // hf_config.num_heads,
"n_heads": hf_config.num_heads,
"d_mlp": hf_config.hidden_size * 4,
"n_layers": hf_config.num_layers,
"n_ctx": hf_config.max_position_embeddings,
"eps": hf_config.layer_norm_epsilon,
"d_vocab": hf_config.vocab_size,
"attn_types": hf_config.attention_layers,
"act_fn": hf_config.activation_function,
"use_attn_scale": False,
"use_local_attn": True,
"window_size": hf_config.window_size,
"scale_attn_by_inverse_layer_idx": False,
"normalization_type": "LN",
}
elif architecture == "GPT2LMHeadModel":
cfg_dict = {
"d_model": hf_config.n_embd,
"d_head": hf_config.n_embd // hf_config.n_head,
"n_heads": hf_config.n_head,
"d_mlp": hf_config.n_embd * 4,
"n_layers": hf_config.n_layer,
"n_ctx": hf_config.n_ctx,
"eps": hf_config.layer_norm_epsilon,
"d_vocab": hf_config.vocab_size,
"act_fn": hf_config.activation_function,
"use_attn_scale": True,
"use_local_attn": False,
"scale_attn_by_inverse_layer_idx": hf_config.scale_attn_by_inverse_layer_idx,
"normalization_type": "LN",
}
elif architecture == "OPTForCausalLM":
cfg_dict = {
"d_model": hf_config.hidden_size,
"d_head": hf_config.hidden_size // hf_config.num_attention_heads,
"n_heads": hf_config.num_attention_heads,
"d_mlp": hf_config.ffn_dim,
"n_layers": hf_config.num_hidden_layers,
"n_ctx": hf_config.max_position_embeddings,
"eps": 1e-5,
"d_vocab": hf_config.vocab_size,
"act_fn": hf_config.activation_function,
"use_attn_scale": True,
"use_local_attn": False,
"scale_attn_by_inverse_layer_idx": False,
"normalization_type": "LN",
}
elif architecture == "GPTJForCausalLM":
cfg_dict = {
"d_model": hf_config.n_embd,
"d_head": hf_config.n_embd // hf_config.n_head,
"n_heads": hf_config.n_head,
"d_mlp": 4 * hf_config.n_embd,
"n_layers": hf_config.n_layer,
"n_ctx": hf_config.n_positions,
"eps": 1e-5,
"d_vocab": hf_config.vocab_size,
"act_fn": hf_config.activation_function,
"use_attn_scale": True,
"use_local_attn": False,
"scale_attn_by_inverse_layer_idx": False,
"parallel_attn_mlp": True,
"positional_embedding_type": "rotary",
"rotary_dim": hf_config.rotary_dim,
"rotary_adjacent_pairs": True,
"normalization_type": "LN",
}
elif architecture == "GPTNeoXForCausalLM":
cfg_dict = {
"d_model": hf_config.hidden_size,
"d_head": hf_config.hidden_size // hf_config.num_attention_heads,
"n_heads": hf_config.num_attention_heads,
"d_mlp": hf_config.intermediate_size,
"n_layers": hf_config.num_hidden_layers,
"n_ctx": hf_config.max_position_embeddings,
"eps": hf_config.layer_norm_eps,
"d_vocab": hf_config.vocab_size,
"act_fn": hf_config.hidden_act,
"use_attn_scale": True,
"use_local_attn": False,
"scale_attn_by_inverse_layer_idx": False,
"parallel_attn_mlp": True,
"positional_embedding_type": "rotary",
"rotary_adjacent_pairs": False,
"normalization_type": "LN",
}
rotary_pct = hf_config.rotary_pct
cfg_dict["rotary_dim"] = round(rotary_pct * cfg_dict["d_head"])
elif architecture == "BertForMaskedLM":
cfg_dict = {
"d_model": hf_config.hidden_size,
"d_head": hf_config.hidden_size // hf_config.num_attention_heads,
"n_heads": hf_config.num_attention_heads,
"d_mlp": hf_config.intermediate_size,
"n_layers": hf_config.num_hidden_layers,
"n_ctx": hf_config.max_position_embeddings,
"eps": hf_config.layer_norm_eps,
"d_vocab": hf_config.vocab_size,
"act_fn": "gelu",
"attention_dir": "bidirectional",
}
elif architecture == "MistralForCausalLM":
cfg_dict = {
"d_model": 4096,
"d_head": 4096 // 32,
"n_heads": 32,
"d_mlp": 14336,
"n_layers": 32,
"n_ctx": 2048, # Capped due to memory issues
"d_vocab": 32000,
"act_fn": "silu",
"normalization_type": "RMS",
"positional_embedding_type": "rotary",
"window_size": 4096,
"attn_types": ["local"] * 32,
"eps": 1e-05,
"n_key_value_heads": 8,
"gated_mlp": True,
"use_local_attn": True,
"rotary_dim": 4096 // 32,
}
elif architecture == "MixtralForCausalLM":
cfg_dict = {
"d_model": hf_config.hidden_size,
"d_head": hf_config.hidden_size // hf_config.num_attention_heads,
"n_heads": hf_config.num_attention_heads,
"d_mlp": hf_config.intermediate_size,
"n_layers": hf_config.num_hidden_layers,
"n_ctx": 2048, # hf_config.max_position_embeddings, # Capped due to memory issues
"d_vocab": hf_config.vocab_size,
"act_fn": hf_config.hidden_act,
"normalization_type": "RMS",
"positional_embedding_type": "rotary",
"window_size": hf_config.sliding_window, # This is None, as no sliding window was used
"attn_types": ["global"] * 32,
"eps": hf_config.rms_norm_eps,
"n_key_value_heads": hf_config.num_key_value_heads,
"gated_mlp": True,
"use_local_attn": False,
"rotary_dim": hf_config.hidden_size // hf_config.num_attention_heads,
"num_experts": hf_config.num_local_experts,
"experts_per_token": hf_config.num_experts_per_tok,
}
elif architecture == "BloomForCausalLM":
cfg_dict = {
"d_model": hf_config.hidden_size,
"d_head": hf_config.hidden_size // hf_config.n_head,
"n_heads": hf_config.n_head,
"d_mlp": hf_config.hidden_size * 4,
"n_layers": hf_config.n_layer,
"n_ctx": 2048, # Capped due to HF Tokenizer Constraints
"d_vocab": hf_config.vocab_size,
"act_fn": "gelu_fast",
"eps": hf_config.layer_norm_epsilon,
"normalization_type": "LN",
"post_embedding_ln": True,
"positional_embedding_type": "alibi",
}
elif architecture == "GPT2LMHeadCustomModel":
# santacoder
cfg_dict = {
"d_model": hf_config.n_embd,
"d_head": hf_config.n_embd // hf_config.n_head,
"n_heads": hf_config.n_head,
"d_mlp": hf_config.n_embd * 4,
"n_layers": hf_config.n_layer,
"n_ctx": hf_config.n_positions,
"eps": hf_config.layer_norm_epsilon,
"d_vocab": hf_config.vocab_size,
"act_fn": hf_config.activation_function,
"use_attn_scale": True,
"use_local_attn": False,