Edit model card


crystal coder logo

CrystalCoder is a 7B parameter language model, distinctively trained on the SlimPajama and StarCoder datasets. This model excels in balancing natural language processing and coding capabilities. Despite being trained on a smaller dataset of 1.4 trillion tokensβ€”compared to LLaMA 2's 2 trillionβ€”CrystalCoder surpasses LLaMA 2 in some challenging English and coding tasks. It demonstrates superior performance in benchmarks like MMLU, HumanEval, and MBPP. By comparing CrystalCoder with other similar work, CrystalCoder is quite balance on language and coding tasks.

performance in benchmarks
Performance on Standard Benchmarks
performance radar chart


  • We compute all evaluation metrics ourselves.

  • Language benchmarks are computed following the convention of the Huggingface Leaderboard, which means AI2 Reasoning Challenge in 25-shot, HellaSwag in 10-shot, MMLU computed in 5-shot, TruthfulQA in 0-shot.

  • As reported in prior work, the choice of temperature affect the programming metrics a lot, we evaluate all models with the following temperature:

    • Scores for HumanEval is computed with a temperature of 0.2
    • Scores for MBPP is computed with a temperature of 0.1
  • For detailed token breakdown of CrystalCoder dataset, refer to the CrystalCoder dataset repository.

About LLM360

LLM360 is an initiative for comprehensive and fully open-sourced LLMs, where all training details, model checkpoints, intermediate results, and additional analyses are made available to the community. Our goal is to advance the field by inviting the community to deepen the understanding of LLMs together. As the first step of the project LLM360, we release all intermediate model checkpoints, our fully-prepared pre-training dataset, all source code and configurations, and training details. We are committed to continually pushing the boundaries of LLMs through this open-source effort.

Get access now at LLM360 site

🟣 Model Description

🟣 Model Architecture

CrystalCoder leverages a GPT-like architecture, akin to LLaMA, but with the addition of maximal update parameterization (muP).

Key modifications introduced by muP include:

  1. Input embeddings are scaled by mup_embeddings_scale.
  2. Output logits are scaled by mup_output_alpha * mup_width_scale.
  3. Attention weights scaling is refined to division by the hidden dimension size ((QK^T)/d) instead of its square root ((QK^T)/sqrt(d)).
  4. Learning rates and weight decay are optimized for different parameter groups:
    • Embedding layer: LR=BASE_LR, WD=BASE_WD.
    • Normalization layers: LR=BASE_LR, WD=0.
    • Other Parameters: LR=BASE_LR * mup_width_scale, WD=BASE_WD.
  5. Initialization ranges are determined based on muP hyperparameters.

The muP hyperparameters are set as follows:

  • mup_embeddings_scale: 14.6
  • mup_output_alpha: 2.22
  • mup_width_scale: 0.0625

For other architecture choices:

  • We use LayerNorm instead of RMSNorm.
  • Rotary position embeddings applied to only the first 25% of hidden dimensions.
  • Training sequence length is 2048.
  • Embedding dimension is 32032.

🟣 Tokenization

Our tokenizer is based on the LLaMA tokenizer, with 22 additional special tokens for the following usage:

  • 4 filling-in-middle (FIM) tokens such as <|fim_prefix|> to support FIM inference.
  • 14 spcial tokens such as <|filename|>, <|jupyter_start|>, <|reponame|> to support meta data for code dataset following StarCoder's method.
  • 4 special tokens such as <|sys_start|>, <|im_start|> to support instruction tuning.

Therefore, we extended the LLaMA tokenizer vocabulary size from 32000 to 32032. Some token ids are reserved and not used.

🟣 Training

Our training has 3 stages:

  • Stage 1: Pretraining on first half of SlimPajama (50% x 690B = 345B).
  • Stage 2: Pretraining on the other half of SlimPajama (50% x 690B = 345B), plus two epochs of StarCoder Data (2 x 291B).
  • Stage 3: Pretraining on 100B additional Python and web-related data (HTML, JavaScript, CSS) sampled from StarCoder Data, and 10B tokens sampled from SlimPajama.

For details of the training dataset for each stage, please refer to the Dataset section and our CrystalCoder Data Card.

For hyperparameters used in each stage, please refer to the following table:

hyperparameter table

For more details of training, please refer to our paper.

🟣 Dataset

Our tokenized datasets for all phases are available at CrystalCoderDatasets.

🟣 Model Usage

To load a specific checkpoint, use the revision argument as shown below, for example, CrystalCoder_phase1_checkpoint_055500. All the revisions can be seen from the branch dropdown in the "Files and versions" tab. If no revision argument is provided, it will load the phase 3 final checkpoint CrystalCoder_phase3_checkpoint_027728.

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(
model = AutoModelForCausalLM.from_pretrained(

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

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

print("-"*20 + "Output for model"  + 20 * '-')

🟣 Completion Example:


from typing import List
def has_close_elements(numbers: List[float], threshold: float) -> bool:
""" Check if in given list of numbers, are any two numbers closer to each other than given threshold.
>>> has_close_elements([1.0, 2.0, 3.0], 0.5) False
>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) True """


from typing import List
def has_close_elements(numbers: List[float], threshold: float) -> bool:
""" Check if in given list of numbers, are any two numbers closer to each other than given threshold.
>>> has_close_elements([1.0, 2.0, 3.0], 0.5) False
>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) True """

# Fill in this function. It should return the index into `numbers` where the closest pair should be inserted.
def closest_pair(numbers: List[float], threshold: float) -> int:
""" Find the closest pair in a given list ofalso numbers.

    Assumes all the numbers are numbers in the list are positive.
    Returns the correct index into `numbers` where the closest pair should be inserted. This
    number is the *first* element of the closest pair.

>>> closest_pair([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.25) 1
>>> closest_pair([12.8, 12.0], 0.0) 0
>>> closest_pair([12.8, 12.0, 12.5, 12.1], 0.0) 1
>>> closest_pair([12.8, 11.5, 12.0, 12.5, 12.1], 0.0) 2 """

<unk> import torch
import numpy as np

🟣 Training Logs and Evaluation Results

Please refer to our W&B project page for complete training logs and evaluation results.

Selected Metrics are displayed below.

HumanEval MBPP
humaneval mbpp
ARC HellaSwag
arc hellaswag
MMLU TruthfulQA
mmlu truthfulqa

🟣 CrystalCoder-Instruct

We also have instruction tuned versions of CrystalCoder, based on stage 2 and stage 3 final checkpoints. The Instruct version will be released later.

🟣 Citation


      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},
Downloads last month
Model size
6.58B params
Tensor type
Inference Examples
Inference API (serverless) does not yet support model repos that contain custom code.

Space using LLM360/CrystalCoder 1

Collection including LLM360/CrystalCoder