416 lines
15 KiB
Markdown
416 lines
15 KiB
Markdown
---
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widget:
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- example_title: SUS-Chat
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text: hi
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output:
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text: ' Hello! How can I assist you today?'
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pipeline_tag: text-generation
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license: apache-2.0
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---
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# 🐷SUS-Chat: Instruction tuning done right
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<p align="left">
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<a href="README_CN.md">中文</a>  ｜  English 
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</p>
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<br><br>
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<div align="center">
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<p align="center">
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<img src="https://github.com/SUSTech-IDEA/SUS-Chat/raw/main/assets/sustech.svg?sanitize=true" width="200px">
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<img src="https://github.com/SUSTech-IDEA/SUS-Chat/raw/main/assets/ccnl.png?sanitize=true" width="200px">
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</p>
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<div style="display: inline-block;">
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<a rel="noopener nofollow" href="https://github.com/SUSTech-IDEA/SUS-Chat/issues">
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<img src="https://img.shields.io/github/issues/SUSTech-IDEA/SUS-Chat?logo=github" style="margin: 0 0;">
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</a>
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</div>
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<div style="display: inline-block;">
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<a href="https://huggingface.co/SUSTech">
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<img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-SUSTech-blue" style="margin: 0 0;">
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</a>
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</div>
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<div style="display: inline-block;">
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<a rel="noopener nofollow" href="https://www.modelscope.cn/organization/sustc/">
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<img src="https://img.shields.io/badge/🤖ModelScope-sustc-blue" style="margin: 0 0;">
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</a>
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</div>
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<a href="https://wisemodel.cn/organization/SUSTech">
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<img src="https://img.shields.io/badge/WiseModel-SUSTech-blue"> </a>
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<div style="display: inline-block;">
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<a rel="noopener nofollow" href="https://github.com/SUSTech-IDEA/SUS-Chat/blob/main/LICENSE">
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<img src="https://img.shields.io/badge/Code_License-Apache_2.0-lightblue" style="margin: 0 0;">
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</a>
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</div>
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<div style="display: inline-block;">
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<a rel="noopener nofollow" href="https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt">
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<img src="https://img.shields.io/badge/Model_License-Model_Agreement-lightblue" style="margin: 0 0;">
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</a>
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</div>
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<div style="display: inline-block;">
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<a rel="noopener nofollow" href="mailto:oss@data.sustech.edu.cn">
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<img src="https://img.shields.io/badge/✉️-data@sustech.edu.cn-FFE01B" style="margin: 0 0;">
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</a>
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</div>
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</div>
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# News
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- 2024-1-04: 🔥 `cloudyu` created a series of top ranked
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[MOE](https://huggingface.co/cloudyu/Yi-34Bx2-MoE-60B) based on our
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model
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- 2023-12-09: 🔥 `Tigerbot` variant has been
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[deleted](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/438),
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`SUS-Chat-34B` is now the the top-ranked LLaMA model and the
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top-ranked chat model.
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- 2023-12-07: SUS-Chat-34B is now available on
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[WiseModel🧠](https://wisemodel.cn/model/SUSTech/SUS-Chat-34B).
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- 2023-12-06: Try [SUS-Chat-34B
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chat-ui](https://huggingface.co/spaces/SUSTech/SUS-Chat-34B).
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- 2023-12-05: SUS-Chat-34B is now available on
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[ModelScope🤖](https://www.modelscope.cn/models/SUSTC/SUS-Chat-34B/summary)
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- 2023-12-05: SUS-Chat-34B is ranked 2nd in [Open LLM
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leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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and surpassed all models under 70B.
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- 2023-12-01: SUS-Chat-34B is now available on
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[HuggingFace🤗](https://huggingface.co/SUSTech/SUS-Chat-34B).
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# Introduction
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<img src="https://hackmd.io/_uploads/HJlDtzhBa.png" id="fig-sus"
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alt="Figure 1: DALL·E 2023-12-01 11.03.28 - An imposing, majestic wild boar combined with elements of a futuristic transformer robot. The boar itself should be intricately blended with these tra" />
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**SUS-Chat-34B** is a 34B bilingual Chinese-English dialogue model,
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jointly released by the **[Southern University of Science and
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Technology](https://huggingface.co/SUSTech)** and
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**[IDEA-CCNL](https://huggingface.co/IDEA-CCNL)**. This model is based
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on [`01-ai/Yi-34B`](https://huggingface.co/01-ai/Yi-34B) and has been
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fine-tuned on millions of high-quality, multilingual instruction data.
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While maintaining the strong language capabilities of the base model,
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the SUS-Chat-34B model has improved the model’s response to human
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instructions through high-quality instruction fine-tuning and excels at
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imitating human thought processes through chains of thought. It
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introduces inter-instruction attention sharing in long texts, expanding
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the window size from 4K to 8K, significantly enhancing the usability of
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multi-turn dialogues.
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It has surpassed all models of the same size in almost all benchmark
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tests and is better suited to meet the practical needs of complex
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multilingual tasks. Compared to larger models, SUS-Chat-34B remains
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highly competitive and has achieved state-of-the-art performance in our
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comprehensive evaluations.
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SUS-Chat-34B model has the following highlights:
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1. Large-scale complex instruction following data: Trained with 1.4
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billion tokens of high-quality complex instruction data, covering
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Chinese and English, multi-turn dialogues, mathematics, reasoning,
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and various other types of instruction data;
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2. Strong performance in general tasks: The SUS-Chat-34B model excels
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in numerous mainstream Chinese and English tasks, surpassing other
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open-source instruction fine-tuned models of the same parameter
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scale. It also competes well against models with larger parameter
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scales;
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3. Longer context window and excellent multi-turn dialogue
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capabilities: Currently, SUS-Chat-34B supports an 8K context window,
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and is trained with a large amount of multi-turn instruction and
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single-multi-turn mixed data, demonstrating remarkable capabilities
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in long-text dialogue information focus and instruction follow-up.
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SUS-Chat powerfully demonstrates that through the right instruction
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fine-tuning, academic institutions can achieve better performance
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without increasing model parameters, using open-source datasets and
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models. This bridges the gap between academia and industry in large
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language models and opens new possibilities for collaboration between
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academic and industrial sectors.
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# Performance
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To better evaluate the performance of the SUS-Chat-34B model, we
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conducted assessments across multiple benchmark tests and have
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open-sourced the evaluation framework
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[TLEM](https://huggingface.co/spaces/SUSTech/tlem) to facilitate
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replication and comparison by other researchers.
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In TLEM, we utilized various benchmark tests including MMLU, CMMLU,
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C-Eval, BBH, GSM-8K, and MATH, to measure the model’s knowledge and
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thinking capabilities. In these metrics, the SUS-Chat-34B model achieved
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state-of-the-art performance. Additionally, we incorporated
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[lm-eval](https://github.com/EleutherAI/lm-evaluation-harness) to test
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SUS-Chat and similar models on winogrande, hellaswag, arc, and
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truthful-qa, assessing the model’s common-sense reasoning ability and
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susceptibility to illusions.
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Overall, the SUS-Chat-34B model significantly outperformed models of
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similar scale and achieved the most advanced comprehensive performance.
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<img
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src="https://github.com/SUSTech-IDEA/SUS-Chat/raw/main/assets/radar.png"
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id="fig-bench" alt="Figure 2: Benchmark" />
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<div>
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<table>
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<colgroup>
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<col style="width: 50%" />
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<col style="width: 50%" />
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</colgroup>
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<tbody>
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<tr class="odd">
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<td style="text-align: center;"><div width="50.0%"
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data-layout-align="center">
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<h2 id="english-understanding">English Understanding</h2>
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<table>
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<thead>
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<tr class="header">
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<th style="text-align: right;">Model</th>
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<th style="text-align: center;">mmlu (0-shot)</th>
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</tr>
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</thead>
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<tbody>
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<tr class="odd">
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<td style="text-align: right;">GPT-4</td>
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<td style="text-align: center;">83</td>
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</tr>
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<tr class="even">
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<td style="text-align: right;">SUS-Chat-34B</td>
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<td style="text-align: center;"><u>74.35</u></td>
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</tr>
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<tr class="odd">
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<td style="text-align: right;">Qwen-72b-Chat</td>
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<td style="text-align: center;"><strong>74.52</strong></td>
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</tr>
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<tr class="even">
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<td style="text-align: right;">Deepseek-68b-Chat</td>
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<td style="text-align: center;">69.43</td>
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</tr>
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<tr class="odd">
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<td style="text-align: right;">OrionStar-Yi-34B-Chat</td>
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<td style="text-align: center;">68.51</td>
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</tr>
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<tr class="even">
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<td style="text-align: right;">Yi-34B-Chat</td>
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<td style="text-align: center;">66.96</td>
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</tr>
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</tbody>
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</table>
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</div></td>
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<td style="text-align: center;"><div width="50.0%"
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data-layout-align="center">
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<h2 id="chinese-capabilities">Chinese Capabilities</h2>
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<table>
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<colgroup>
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<col style="width: 34%" />
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<col style="width: 32%" />
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<col style="width: 32%" />
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</colgroup>
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<thead>
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<tr class="header">
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<th style="text-align: right;">Model</th>
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<th style="text-align: center;">cmmlu (0-shot)</th>
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<th style="text-align: center;">C-Eval (0-shot)<a href="#fn1"
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class="footnote-ref" id="fnref1"
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role="doc-noteref"><sup>1</sup></a></th>
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</tr>
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</thead>
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<tbody>
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<tr class="odd">
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<td style="text-align: right;">GPT-4</td>
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<td style="text-align: center;">71</td>
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<td style="text-align: center;">69.9</td>
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</tr>
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<tr class="even">
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<td style="text-align: right;">SUS-Chat-34B</td>
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<td style="text-align: center;"><strong>78.68</strong></td>
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<td style="text-align: center;"><strong>82.42</strong></td>
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</tr>
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<tr class="odd">
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<td style="text-align: right;">Qwen-72b-Chat</td>
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<td style="text-align: center;"><u>77.02</u></td>
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<td style="text-align: center;"><u>77.22</u></td>
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</tr>
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<tr class="even">
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<td style="text-align: right;">Deepseek-68b-Chat</td>
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<td style="text-align: center;">48.51</td>
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<td style="text-align: center;">59.7</td>
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</tr>
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<tr class="odd">
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<td style="text-align: right;">OrionStar-Yi-34B-Chat</td>
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<td style="text-align: center;">66.88</td>
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<td style="text-align: center;">65.13</td>
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</tr>
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<tr class="even">
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<td style="text-align: right;">Yi-34B-Chat</td>
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<td style="text-align: center;">55.16</td>
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<td style="text-align: center;">77.16</td>
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</tr>
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</tbody>
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</table>
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</div></td>
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</tr>
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</tbody>
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</table>
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<section id="footnotes" class="footnotes footnotes-end-of-document"
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role="doc-endnotes">
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<hr />
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<ol>
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<li id="fn1"><p>C-Eval results are evaluated on the validation
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datasets<a href="#fnref1" class="footnote-back"
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role="doc-backlink">↩︎</a></p></li>
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</ol>
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</section>
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</div>
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## Math & Reasoning
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| Model | gsm8k (0-shot) | MATH (0-shot) | BBH (0-shot) |
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|----------------------:|:--------------:|:-------------:|:------------:|
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| GPT-4 | 91.4 | 45.8 | 86.7 |
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| SUS-Chat-34B | **80.06** | 28.7 | 67.62 |
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| Qwen-72b-Chat | <u>76.57</u> | **35.9** | **72.63** |
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| Deepseek-68b-Chat | 74.45 | <u>29.56</u> | <u>69.73</u> |
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| OrionStar-Yi-34B-Chat | 54.36 | 12.8 | 62.88 |
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| Yi-34B-Chat | 63.76 | 10.02 | 61.54 |
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## More Tasks
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| Model | winogrande (5-shot) | arc (25-shot) | hellaswag (10-shot) | TruthfulQA mc1 (0-shot) | TruthfulQA mc2 (0-shot) |
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|----------------------:|:-------------------:|:-------------:|:-------------------:|:-----------------------:|:-----------------------:|
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| GPT-4 | — | 94.5 | 91.4 | 59.00 | — |
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| SUS-Chat-34B | **81.22** | <u>81.54</u> | 83.79 | **40.64** | **57.47** |
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| Qwen-72b-Chat | 76.09 | **82.10** | <u>86.06</u> | 39.17 | <u>56.37</u> |
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| Deepseek-68b-Chat | <u>80.58</u> | 81.29 | **87.02** | <u>40.02</u> | 50.64 |
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| OrionStar-Yi-34B-Chat | 77.27 | 80.19 | 84.54 | 36.47 | 53.24 |
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| Yi-34B-Chat | 76.64 | 70.66 | 82.29 | 38.19 | 54.57 |
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## Overall
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| Model | Average |
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|----------------------:|:---------:|
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| SUS-Chat-34B | **69.05** |
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| Qwen-72b-Chat | 68.41 |
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| Deepseek-68b-Chat | 62.91 |
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| OrionStar-Yi-34B-Chat | 60.21 |
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| Yi-34B-Chat | 59.72 |
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To reproduce the results, please start a corresponding vllm server and
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refer to
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[here](https://sustech-tlem.static.hf.space/index.html#start-evaluating-your-model-in-3-line).
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# Usage
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SUS-Chat-34B is a standard LLaMA model and should be seamlessly
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compatible with the LLaMA ecosystem. We provide the following example to
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demonstrate how it can be used for multi-turn dialogues.
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Feel free to [open an
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issue](https://github.com/SUSTech-IDEA/SUS-Chat/issues) if you have any
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questions.
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``` python
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from transformers import AutoModelForCausalLM, AutoTokenizer # 🤗 Transformers, or
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# from modelscope import AutoModelForCausalLM, AutoTokenizer # 🤖 ModelScope
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def chat_template(messages):
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history = ""
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for message in messages:
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match message:
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case {"role": "user", "content": message}:
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history += f"### Human: {message}\n\n### Assistant: "
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case {"role": "assistant", "content": message}:
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history += message
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return history
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model_path = "SUSTech/SUS-Chat-34B"
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# model_path = "SUSTC/SUS-Chat-34B" # ModelScope
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tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
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model = AutoModelForCausalLM.from_pretrained(
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model_path, device_map="auto", torch_dtype="auto"
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).eval()
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messages = [{"role": "user", "content": "hi"}]
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input_ids = tokenizer.encode(
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chat_template(messages), return_tensors="pt", add_special_tokens=False
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).to("cuda")
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output_ids = model.generate(input_ids.to("cuda"), max_length=256)
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response = tokenizer.decode(
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output_ids[0][input_ids.shape[1] :], skip_special_tokens=False
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)
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messages.append({"role": "assistant", "content": response})
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# Second round
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messages.append({"role": "user", "content": "What is the capital of China?"})
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input_ids = tokenizer.encode(
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chat_template(messages), return_tensors="pt", add_special_tokens=False
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).to("cuda")
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output_ids = model.generate(input_ids.to("cuda"), max_length=256)
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response = tokenizer.decode(
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output_ids[0][input_ids.shape[1] :], skip_special_tokens=False
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)
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messages.append({"role": "assistant", "content": response})
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```
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# Limitations
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SUS-Chat has only undergone supervised fine-tuning and has not yet been
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trained on human preference learning. As a result, it may produce
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unreasonable responses in some situations and exacerbate existing issues
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in language models, including hallucinations, non-determinism, and
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cumulative errors. To achieve better performance for downstream tasks,
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we recommend adjusting the generation configuration parameters
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accordingly.
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# Disclaimer
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During the training process, we used data compliance check algorithms to
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ensure the compliance of the training model as much as possible. Due to
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the complexity of the data and the diverse use cases of language models,
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we cannot guarantee that the model will produce correct and reasonable
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outputs in all scenarios. Please be aware that there is still a risk of
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the model generating problematic outputs. We will not be responsible for
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any risks or issues arising from misuse, misguidance, illegal use, and
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related misinformation, as well as data security issues related to the
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model.
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# License
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This model is developed entirely for academic research and free
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commercial use, but it must adhere to the
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[license](https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt)
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from [01-ai](https://huggingface.co/01-ai).
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