README.md

metadata

license: apache-2.0
language:
  - en

ARCHIVED.

Download from original repo: https://huggingface.co/openlm-research/open_llama_3b_600bt_preview

I made a few PRs to the original repo to include my changes!

Original model from https://huggingface.co/openlm-research/open_llama_3b_600bt_preview. Example below edited from https://github.com/openlm-research/open_llama

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "openlm-research/open_llama_3b_600bt_preview"
fast_model_name = "danielhanchen/open_llama_3b_600bt_preview"

tokenizer = AutoTokenizer.from_pretrained(fast_model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype = torch.float16, device_map = "auto")

prompt = "Q: What is the largest animal?\nA:"
input_ids = tokenizer(prompt, return_tensors = "pt").input_ids
print( tokenizer.decode( model.generate( input_ids, max_new_tokens = 32).ravel() ) )

This repo includes:

1. Ported LlamaTokenizer to LlamaTokenizerFast via a few lines of code. Loading via AutoTokenizer takes 4 to 5 minutes. Now, a few seconds! Essentially the porting is done via the below code:

# from huggingface_hub import notebook_login
# notebook_login()
from transformers import LlamaTokenizerFast
from tokenizers import AddedToken
tokenizer = LlamaTokenizerFast.from_pretrained(
    "openlm-research/open_llama_3b_600bt_preview",
    add_bos_token = True,
    add_eos_token = False, # Original LLaMA is False -> add </s> during processing.
    bos_token = AddedToken("<s>",   single_word = True),
    eos_token = AddedToken("</s>",  single_word = True),
    unk_token = AddedToken("<unk>", single_word = True),
    pad_token = AddedToken("<unk>", single_word = True)
)
tokenizer.push_to_hub("open_llama_3b_600bt_preview")

2. AutoTokenizer does not recognize the BOS, EOS and UNK tokens. Weirdly <unk> ie the 0 token was added instead of the <s> or </s> token.
3. Manually added BOS <s>, EOS </s>, UNK <unk> tokens, with PAD (padding) being also the <unk> token.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	32.13
ARC (25-shot)	36.86
HellaSwag (10-shot)	59.96
MMLU (5-shot)	25.97
TruthfulQA (0-shot)	32.81
Winogrande (5-shot)	63.69
GSM8K (5-shot)	0.61
DROP (3-shot)	5.0