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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- llm |
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- code |
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--- |
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# CrystalChat |
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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. |
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As always, the training data, training code, and metrics are publicly available. |
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## About LLM360 |
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LLM360 is an initiative for comprehensive and fully open-sourced LLMs, |
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where all training details, model checkpoints, intermediate results, and |
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additional analyses are made available to the community. Our goal is to advance |
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the field by inviting the community to deepen the understanding of LLMs |
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together. As the first step of the project LLM360, we release all intermediate |
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model checkpoints, our fully-prepared pre-training dataset, all source code and |
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configurations, and training details. We are |
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committed to continually pushing the boundaries of LLMs through this open-source |
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effort. |
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Get access now at [LLM360 site](https://www.llm360.ai/) |
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# CrystalChat Performance |
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| 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) | |
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|:------------------------:|:--------------:|:------------:|:-------------:|:-----------:|:-----:|:---------:|:-------------:|:-----:|:------------------:|:----------:|:------------------:|:-------------:| |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| AmberChat 7B | 1.25T | - | 44.76 | - | 42.83 | 74.03 | 38.88 | 5.31 | 66.77 | 40.72 | - | - | |
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| Combined Language and Coding Ability | |
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|------------------------------------------------| |
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<img src="CC-Compare.jpg" alt="arc" width="800"/> |
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| Performance on Standard Benchmarks | |
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|------------------------------------------------| |
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<img src="cc-eval-std-benchmarks.png" alt="std-bench" width="600"/> |
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| Perforamnce on Language Benchmarks | |
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|---------------------------------------------------------| |
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<img src="cc-eval-lang-compare.png" alt="arc" width="600"/> |
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## Model Description |
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- **Model type:** Language model with the same architecture as LLaMA-7B |
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- **Language(s) (NLP):** English |
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- **License:** Apache 2.0 |
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- **Resources for more information:** |
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- [Training Code](https://github.com/LLM360/crystalcoder-train) |
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- [Data Preparation](https://github.com/LLM360/crystalcoder-data-prep) |
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- [Metrics](https://github.com/LLM360/Analysis360) |
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- [Fully processed CrystalCoder pretraining data](https://huggingface.co/datasets/LLM360/CrystalCoderDatasets) |
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# Loading CrystalChat |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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tokenizer = AutoTokenizer.from_pretrained("LLM360/CrystalChat", trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("LLM360/CrystalChat", trust_remote_code=True).to(device) |
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prompt = 'int add(int x, int y) {' |
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device) |
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gen_tokens = model.generate(input_ids, do_sample=True, max_length=400) |
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print("-"*20 + "Output for model" + 20 * '-') |
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print(tokenizer.batch_decode(gen_tokens)[0]) |
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``` |
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# Bias, Risks, and Limitations |
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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. |
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# Citation |
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**BibTeX:** |
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```bibtex |
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@misc{liu2023llm360, |
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title={LLM360: Towards Fully Transparent Open-Source LLMs}, |
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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}, |
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year={2023}, |
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eprint={2312.06550}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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