Colossal-LLaMA-2-7B

🎉 We released Colossal-LLaMA-2-7B-base based on LLaMA-2 !!

｜🔥 GitHub |

Table of Contents

• Model Introduction
• Usage
• Performance Evaluation
• Technical Insights
  • Data
  • Tokenizer
  • Training Logs
  • Training Strategy
    • Multi-stage Training
    • Bucket-based Training
• Limitations
• Citations

Model Introduction

The Colossal-AI team has introduced the open-source model Colossal-LLaMA-2-7B-base. This model, a derivation of LLaMA-2, has undergone continual pre-training involving approximately 8.5 billion tokens over a duration of 15 hours with 64 A800 GPUs. At a cost of less than $1,000, you can achieve results similar to those that cost millions of dollars to pretrain from scratch. It is licensed under the LLaMA-2 license and Apache 2.0 License without any additional commercial use restrictions. This solution can also be used to build models of specific domain knowledge or tasks.

Colossal-LLaMA-2-7B-base is designed to accommodate both the Chinese and English languages, featuring an expansive context window spanning 4096 tokens. Remarkably, it has exhibited exceptional performance when benchmarked against models of equivalent scale in standard Chinese and English evaluation metrics, including C-Eval and MMLU, among others.

Usage

To load Colossal-LLaMA-2-7B-base model using Transformers, use the following code:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("hpcai-tech/Colossal-LLaMA-2-7b-base", device_map="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("hpcai-tech/Colossal-LLaMA-2-7b-base", trust_remote_code=True)
input = "明月松间照，\n\n->\n\n"
inputs = tokenizer(input, return_tensors='pt')
inputs = inputs.to('cuda:0')
pred = model.generate(**inputs,
                        max_new_tokens=512,
                        do_sample=True,
                        temperature=0.3,
                        top_k=50,
                        top_p=0.95,
                        num_return_sequences=1)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True)[len(input):])

Performance Evaluation

Performance Evaluation

We conducted comprehensive evaluation on 4 datasets and compare our Colossal-Llama-2-7b-base model with various models.

• We use 5-shot for MMLU and calculate scores based on the logits of first predicted token.
• We use 5-shot for CMMLU and calculate scores based on the logits of first predicted token.
• We use 5-shot for AGIEval and only calculate scores for 4-choice questions using a combination metric of exact match and the logits of first predicted token. If any of the exact match or logits of first predicted token is correct, the model will get the score.
• We use 0-shot for GAOKAO-Bench and only calculate scores for 4-choice questions based on the logits of first predicted token.
• The generation config for all dataset is greedy search.
• We also provided CEval scores from its latest leaderboard or the official repository of the model.

More details about metrics can be found in Metrics.