File size: 9,356 Bytes
c0c250e c40be13 c0c250e c40be13 887ace8 913e970 b279ee7 913e970 887ace8 c40be13 d6cd77d 2f2a1c8 d6cd77d 6312547 8f9abff 887ace8 7a03621 887ace8 7a03621 c40be13 6be81f3 c40be13 6be81f3 f2ff0be a24e68d c40be13 6be81f3 c40be13 a24e68d f692d0d 36ae419 f692d0d 36ae419 b3cc9d6 887ace8 c40be13 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 |
---
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](https://huggingface.co/LLM360/CrystalCoder). Following the release of [LLM360/AmberChat](https://huggingface.co/LLM360/AmberChat)and [LLM360/AmberSafe](https://huggingface.co/LLM360/AmberSafe) in December 2023, CrystalChat is the next and most performant chat model released under LLM360. CrystalChat is trained on a carefully selected mix publicly available language and code datasets.
As always, the training data, training code, and metrics are publicly available.
## 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](https://www.llm360.ai/)
# Instruction Tuning Training
**CrystalChat** is using the last **CrystalCoder** checkpoint of phase2 ([CrystalCoder_phase2_checkpoint_214387](https://huggingface.co/LLM360/CrystalCoder/tree/CrystalCoder_phase2_checkpoint_214387)) as the initialization checkpoint. We then finetune the model using the dataset mentioned below.
We also performed the same finetuning on the last **CrystalCoder** checkpoint of phase3 ([CrystalCoder_phase3_checkpoint_027728](https://huggingface.co/LLM360/CrystalCoder/tree/CrystalCoder_phase3_checkpoint_027728)). The phase2 and phase3 finetuning results are very similar, but phase2 finetuning exhibits slightly better performance on the English language benchmarks. We choose the phase2 finetuning result as the final model for **CrystalChat**.
# Instruction Tuning Data
The instruction tuning data is a mix of publicly available language and code datasets, plus a orginally created dataset called **WebAlpaca**. The WebAlpaca dataset is created by us and is used as part of our instruction tuning training data. We will release the WebAlpaca dataset in a separate repository.
The summary of the instruction tuning data is as follows:
<center><img src="data_table.jpg" alt="Instruction Data"/></center>
# Instruction Format
We've added some new special tokens to the CrystalCoder tokenizer to support the instruction tuning.
List special tokens used in the instruction tuning:
```
bos: <s>
eos: </s>
system_start: <|sys_start|>
system_end: <|sys_end|>
user_start: <|im_start|>
user_end: <|im_end|>
```
The instruction format is as follows:
```
<s> <|sys_start|> system prompt <|sys_end|> <|im_start|> first user utterance <|im_end|> first model response <|im_start|> next user utterance <|im_end|> next model response </s>
```
# Reproducing the Results
We will realize the training code and the training data soon. Our training code is based on [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), with some modifications to support our training data format and Maximal Update Parametrization (μP).
# CrystalChat Performance
| 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 | - | - |
| Combined Language and Coding Ability |
|------------------------------------------------|
<img src="CC-Compare.jpg" alt="arc" width="800"/>
| Performance on Standard Benchmarks |
|------------------------------------------------|
<img src="cc-eval-std-benchmarks.png" alt="std-bench" width="800"/>
| Perforamnce on Language Benchmarks |
|---------------------------------------------------------|
<img src="cc-eval-lang-compare.png" alt="arc" width="800"/>
## 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:**
- [Training Code](https://github.com/LLM360/crystalcoder-train)
- [Data Preparation](https://github.com/LLM360/crystalcoder-data-prep)
- [Metrics](https://github.com/LLM360/Analysis360)
- [Fully processed CrystalCoder pretraining data](https://huggingface.co/datasets/LLM360/CrystalCoderDatasets)
# Loading CrystalChat
```python
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 = '<s> <|sys_start|> You are an AI assistant. You will be given a task. You must generate a detailed and long answer. <|sys_end|> <|im_start|> Write a python function that takes a list of integers and returns the squared sum of the list. <|im_end|>'
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])
```
Response:
````
Here's a Python function named `squared_sum_list` that takes a list of integers as input and returns the squared sum of the list:
```python
def squared_sum_list(lst):
return sum([num ** 2 for num in lst])
```
The function `squared_sum_list` uses a list comprehension to iterate over each number in the input list `lst` and calculate its square. Then, it uses the `sum` function to accumulate all the squared numbers in a single value - the squared sum of the list.
For example:
```python
numbers = [1, 2, 3, 4, 5]
print(squared_sum_list(numbers)) # Outputs: 55
```
In the above code, the list `[1, 2, 3, 4, 5]` is passed as an argument to the `squared_sum_list` function. The function calculates the sum of the squares of the elements in the list, which is `1 + 4 + 9 + 16 + 25 = 55`. The function then returns this result, which is printed to the console.</s>
````
<!-- ## CrystalChat DataMix
| Subset | Tokens (Billion) |
| ----------- | ----------- |
| OASST1-guanaco | 4.46 |
| SlimOrca | 225.63 |
| ShareGPT | 112.91 |
| Evol-ShareGPT | 85.95 |
| ChatLogs | 29.34 |
| CodeAlpaca | 2.62 |
| Rosetta Code | 7.99 |
| Evol-CodeAlpaca 1 | 73.80 |
| Evol-CodeAlpaca 2 | 34.91 |
| HTML Instruction | 43.67 |
| General Textbooks | 85.59 |
| Programming Books | 395.63 |
| Total | 1102.52 | -->
# Evaluation
Coming Soon!
# Bias, Risks, and Limitations
CrystalChat has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). The training data is known and made available [here](https://huggingface.co/datasets/LLM360/CrystalCoderDatasets). It primarily consists of SlimPajama, StarCoder, and WebCrawl dataset.
# Citation
**BibTeX:**
```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}
}
```
|