README.md · lyogavin/Anima-7B-100K at main

metadata

license: apache-2.0
language:
  - en
tags:
  - llama2
  - 100k
  - 7b

Anima LLM supporting 100K input token length. It's trained based on Llama2 7B, so the license support commercial use!

We carefully curated long QA training dataset from 30k to 100k length to train this model. We also made a lot of memory optimizations to make it scale to 100k tokens.

How to train/infer?

install dependencies

# Please update the path of `CUDA_HOME`
export CUDA_HOME=/usr/local/cuda-11.8
pip install transformers==4.31.0
pip install sentencepiece
pip install ninja
pip install flash-attn --no-build-isolation
pip install git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary
pip install git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/xentropy
pip install evaluate
pip install git+https://github.com/huggingface/[email protected]
pip install wandb

inference

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

base_model = "lyogavin/Anima-7B-100K"
tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(
            base_model,
            torch_dtype=torch.float16,
            trust_remote_code=True,
            device_map="auto", 
        )
model.eval()

prompt = "Where is the capital of US?"
inputs = tokenizer(prompt, return_tensors="pt")

inputs['input_ids'] = inputs['input_ids'].cuda()
inputs['attention_mask'] = inputs['attention_mask'].cuda()

# Generate
generate_ids = model.generate(**inputs, max_new_tokens=30,
                       only_last_logit=True, # to save memory
                       use_cache=False, # when run into OOM, enable this can save memory
                       xentropy=True)
output = tokenizer.batch_decode(generate_ids, 
                                skip_special_tokens=True,
                                clean_up_tokenization_spaces=False)[0]

Training

./run_longer_training.sh

Evaluations

There's almost none evaluation dataset designed for 100k tokens. So we designed/curated some dataset for this model. We compared this model and several other public/private models.

1. longchat topic retrieval

Model	Accuracy
Claude2	0.9
together llama2 32k	0.15
longchat 32k 1.5	0.05
Anima 100K	0.5

2. longchat number retrieval

Model	Accuracy
Claude2	0.85
together llama2 32k	0.2
longchat 32k 1.5	0.05
Anima 100K	0.45

3. Narrative QA in zeroscore

Model	F1
Claude2	0.6187
together llama2 32k	0.3833
longchat 32k 1.5	0.2416
Anima 100K	0.4919

Github

Github repo is here