metadata

license: apache-2.0
language:
  - en
pipeline_tag: text-generation
library_name: transformers
tags:
  - llm
  - code

CrystalChat

We present CrystalChat, an instruction following model finetuned from LLM360/CrystalCoder. Here's a comparison table for some popular chat models.

Model	Trained Tokens	Avg. of Avg.	Language Avg.	Coding Avg.	ARC	HellaSwag	MMLU (5-shot)	GSM8K	Winogrande(5-shot)	TruthfulQA	HumanEval (pass@1)	MBPP (pass@1)
CrystalChat 7B	1.275T	44.96	53.29	36.62	51.71	76.12	53.22	28.05	70.64	47.29	34.12	39.11
Mistral-7B-Instruct-v0.1	-	44.34	54.86	30.62	58.05	75.71	55.56	32.00	74.27	55.90	29.27	31.96
CodeLlama-7b-Instruct	2.5T	40.91	45.29	36.52	43.35	66.14	42.75	15.92	64.33	39.23	34.12	38.91
Llama-2-7b-Chat	2T	34.11	52.86	15.35	53.07	78.39	48.42	18.88	73.09	45.30	13.26	17.43
AmberChat 7B	1.25T	-	44.76	-	42.83	74.03	38.88	5.31	66.77	40.72	-	-

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Model Description

Model type: Language model with the same architecture as LLaMA-7B
Language(s) (NLP): English
License: Apache 2.0
Resources for more information:

Loading CrystalChat

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda:0" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained("LLM360/CrystalChat", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("LLM360/CrystalChat", trust_remote_code=True).to(device)

prompt = 'int add(int x, int y) {'

input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
gen_tokens = model.generate(input_ids, do_sample=True, max_length=400)

print("-"*20 + "Output for model"  + 20 * '-')
print(tokenizer.batch_decode(gen_tokens)[0])

Citation

BibTeX:

@misc{liu2023llm360,
      title={LLM360: Towards Fully Transparent Open-Source LLMs}, 
      author={Zhengzhong Liu and Aurick Qiao and Willie Neiswanger and Hongyi Wang and Bowen Tan and Tianhua Tao and Junbo Li and Yuqi Wang and Suqi Sun and Omkar Pangarkar and Richard Fan and Yi Gu and Victor Miller and Yonghao Zhuang and Guowei He and Haonan Li and Fajri Koto and Liping Tang and Nikhil Ranjan and Zhiqiang Shen and Xuguang Ren and Roberto Iriondo and Cun Mu and Zhiting Hu and Mark Schulze and Preslav Nakov and Tim Baldwin and Eric P. Xing},
      year={2023},
      eprint={2312.06550},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}