Update README.md

README.md

@@ -45,7 +45,7 @@ pipeline_tag: text-generation
 <div style="display: inline-block;">
 
 <a rel="noopener nofollow" href="https://www.modelscope.cn/organization/sustc/">
-<img src="https://img.shields.io/badge/ModelScope-sustc-blue" style="margin: 0 0;">
+<img src="https://img.shields.io/badge/🤖ModelScope-sustc-blue" style="margin: 0 0;">
 </a>
 
 </div>
@@ -78,36 +78,58 @@ pipeline_tag: text-generation
 
 # News
 
-- 2023-12-05: SUS-Chat is ranked 2nd in [Open LLM
+- 2023-12-06: Try [SUS-Chat-34B
+  chat-ui](https://huggingface.co/spaces/SUSTech/SUS-Chat-34B).
+
+- 2023-12-05: SUS-Chat-34B is now available on
+  [ModelScope🤖](https://www.modelscope.cn/models/SUSTC/SUS-Chat-34B/summary)
+
+- 2023-12-05: SUS-Chat-34B is ranked 2nd in [Open LLM
   leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
   and surpassed all models under 70B.
 
-- 2023-12-01: SUS-Chat-34B is now avaliable on HuggingFace🤗.
+- 2023-12-01: SUS-Chat-34B is now available on
+  [HuggingFace🤗](https://huggingface.co/SUSTech/SUS-Chat-34B).
 
-# Inrtoduction
+# Introduction
 
 <img src="https://hackmd.io/_uploads/HJlDtzhBa.png" id="fig-sus"
 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" />
 
-**SUS-Chat** is a 34B bilingual Chinese-English dialogue model, jointly
-released by the **Southern University of Science and Technology** and
-**Cognitive Computing and Natural Language Center of International
-Digital Economy Academy (IDEA-CCNL)**. This model is based on
-`01-ai/Yi-34B` and has been fine-tuned on millions of high-quality,
-multilingual instruction data. While maintaining the strong language
-capabilities of the base model, the SUS-Chat-34B model has improved the
-model’s response to human instructions through high-quality instruction
-fine-tuning and excels at imitating human thought processes through
-chains of thought. It introduces inter-instruction attention sharing in
-long texts, expanding the window size from 4K to 8K, significantly
-enhancing the usability of multi-round dialogues.
+**SUS-Chat-34B** is a 34B bilingual Chinese-English dialogue model,
+jointly released by the **[Southern University of Science and
+Technology](https://huggingface.co/SUSTech)** and
+**[IDEA-CCNL](https://huggingface.co/IDEA-CCNL)**. This model is based
+on [`01-ai/Yi-34B`](https://huggingface.co/01-ai/Yi-34B) and has been
+fine-tuned on millions of high-quality, multilingual instruction data.
+While maintaining the strong language capabilities of the base model,
+the SUS-Chat-34B model has improved the model’s response to human
+instructions through high-quality instruction fine-tuning and excels at
+imitating human thought processes through chains of thought. It
+introduces inter-instruction attention sharing in long texts, expanding
+the window size from 4K to 8K, significantly enhancing the usability of
+multi-turn dialogues.
 
 It has surpassed all models of the same size in almost all benchmark
 tests and is better suited to meet the practical needs of complex
 multilingual tasks. Compared to larger models, SUS-Chat-34B remains
-highly competitive and achieved state-of-the-art performance in our
+highly competitive and has achieved state-of-the-art performance in our
 comprehensive evaluations.
 
+SUS-Chat-34B model has the following highlights: 1. Large-scale complex
+instruction following data: Trained with 1.4 billion tokens of
+high-quality complex instruction data, covering Chinese and English,
+multi-turn dialogues, mathematics, reasoning, and various other types of
+instruction data; 2. Strong performance in general tasks: The
+SUS-Chat-34B model excels in numerous mainstream Chinese and English
+tasks, surpassing other open-source instruction fine-tuned models of the
+same parameter scale. It also competes well against models with larger
+parameter scales; 3. Longer context window and excellent multi-turn
+dialogue capabilities: Currently, SUS-Chat-34B supports an 8K context
+window, and is trained with a large amount of multi-turn instruction and
+single-multi-turn mixed data, demonstrating remarkable capabilities in
+long-text dialogue information focus and instruction follow-up.
+
 SUS-Chat powerfully demonstrates that through the right instruction
 fine-tuning, academic institutions can achieve better performance
 without increasing model parameters, using open-source datasets and
@@ -124,10 +146,9 @@ open-sourced the evaluation framework
 replication and comparison by other researchers.
 
 In TLEM, we utilized various benchmark tests including MMLU, CMMLU,
-C-Eval, BBH, GSM-8K, and MATH, focusing on measuring the model’s
-knowledge and thinking capabilities. In these metrics, the SUS-Chat-34B
-model achieved state-of-the-art performance. Additionally, we
-incorporated
+C-Eval, BBH, GSM-8K, and MATH, to measure the model’s knowledge and
+thinking capabilities. In these metrics, the SUS-Chat-34B model achieved
+state-of-the-art performance. Additionally, we incorporated
 [lm-eval](https://github.com/EleutherAI/lm-evaluation-harness) to test
 SUS-Chat and similar models on winogrande, hellaswag, arc, and
 truthful-qa, assessing the model’s common-sense reasoning ability and
@@ -136,28 +157,176 @@ susceptibility to illusions.
 Overall, the SUS-Chat-34B model significantly outperformed models of
 similar scale and achieved the most advanced comprehensive performance.
 
-| model             | mmlu-chat | cmmlu-chat | ceval-chat | gsm8k |   BBH |  MATH | winogrande |   arc | hellaswag | truthfulqa | average |
-|:------------------|----------:|-----------:|-----------:|------:|------:|------:|-----------:|------:|----------:|-----------:|--------:|
-| GPT-4             |        83 |         71 |       69.9 |  91.4 |  86.7 |  45.8 |       87.5 |  94.5 |      91.4 |        nan | 80.1333 |
-| SUS-Chat-34B      |     77.35 |      78.68 |      82.42 | 80.06 | 67.62 |  28.8 |      81.22 | 81.54 |     83.79 |      57.47 |  71.895 |
-| Qwen-72B-Chat     |     74.52 |      77.02 |      77.22 | 76.57 | 72.63 |  35.9 |      80.58 | 81.29 |     87.02 |      50.64 |  71.339 |
-| DeepSeek-67B-Chat |     69.43 |      48.51 |       59.7 | 74.45 | 69.73 | 29.56 |      76.09 |  82.1 |     86.06 |      56.37 |    65.2 |
-| OrionStar-34B     |     68.51 |      66.88 |      65.13 | 54.36 | 62.88 |  12.8 |      77.27 | 80.19 |     84.54 |      53.24 |   62.58 |
-| Yi-34B-Chat       |     66.96 |      55.16 |      77.16 | 63.76 | 61.54 | 10.02 |      76.64 | 70.66 |     82.29 |      54.57 |  61.876 |
-
 <img
 src="https://github.com/SUSTech-IDEA/SUS-Chat/raw/main/assets/radar.png"
 id="fig-bench" alt="Figure 2: Benchmark" />
 
+<div>
+
+<table>
+<colgroup>
+<col style="width: 50%" />
+<col style="width: 50%" />
+</colgroup>
+<tbody>
+<tr class="odd">
+<td style="text-align: center;"><div width="50.0%"
+data-layout-align="center">
+<h2 id="english-understanding">English Understanding</h2>
+<table>
+<thead>
+<tr class="header">
+<th style="text-align: right;">Model</th>
+<th style="text-align: center;">mmlu (0-shot)</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td style="text-align: right;">GPT-4</td>
+<td style="text-align: center;">83</td>
+</tr>
+<tr class="even">
+<td style="text-align: right;">SUS-Chat-34B</td>
+<td style="text-align: center;"><span
+class="math inline">$\underline{74.35}$</span></td>
+</tr>
+<tr class="odd">
+<td style="text-align: right;">Qwen-72b-Chat</td>
+<td style="text-align: center;"><strong>74.52</strong></td>
+</tr>
+<tr class="even">
+<td style="text-align: right;">Deepseek-68b-Chat</td>
+<td style="text-align: center;">69.43</td>
+</tr>
+<tr class="odd">
+<td style="text-align: right;">OrionStar-Yi-34B-Chat</td>
+<td style="text-align: center;">68.51</td>
+</tr>
+<tr class="even">
+<td style="text-align: right;">Yi-34B-Chat</td>
+<td style="text-align: center;">66.96</td>
+</tr>
+</tbody>
+</table>
+</div></td>
+<td style="text-align: center;"><div width="50.0%"
+data-layout-align="center">
+<h2 id="chinese-capabilities">Chinese Capabilities</h2>
+<table>
+<colgroup>
+<col style="width: 34%" />
+<col style="width: 32%" />
+<col style="width: 32%" />
+</colgroup>
+<thead>
+<tr class="header">
+<th style="text-align: right;">Model</th>
+<th style="text-align: center;">cmmlu (0-shot)</th>
+<th style="text-align: center;">C-Eval (0-shot)<a href="#fn1"
+class="footnote-ref" id="fnref1"
+role="doc-noteref"><sup>1</sup></a></th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td style="text-align: right;">GPT-4</td>
+<td style="text-align: center;">71</td>
+<td style="text-align: center;">69.9</td>
+</tr>
+<tr class="even">
+<td style="text-align: right;">SUS-Chat-34B</td>
+<td style="text-align: center;"><strong>78.68</strong></td>
+<td style="text-align: center;"><strong>82.42</strong></td>
+</tr>
+<tr class="odd">
+<td style="text-align: right;">Qwen-72b-Chat</td>
+<td style="text-align: center;"><span
+class="math inline">$\underline{77.02}$</span></td>
+<td style="text-align: center;"><span
+class="math inline">$\underline{77.22}$</span></td>
+</tr>
+<tr class="even">
+<td style="text-align: right;">Deepseek-68b-Chat</td>
+<td style="text-align: center;">48.51</td>
+<td style="text-align: center;">59.7</td>
+</tr>
+<tr class="odd">
+<td style="text-align: right;">OrionStar-Yi-34B-Chat</td>
+<td style="text-align: center;">66.88</td>
+<td style="text-align: center;">65.13</td>
+</tr>
+<tr class="even">
+<td style="text-align: right;">Yi-34B-Chat</td>
+<td style="text-align: center;">55.16</td>
+<td style="text-align: center;">77.16</td>
+</tr>
+</tbody>
+</table>
+</div></td>
+</tr>
+</tbody>
+</table>
+<section id="footnotes" class="footnotes footnotes-end-of-document"
+role="doc-endnotes">
+<hr />
+<ol>
+<li id="fn1"><p>C-Eval results are evaluated on the validation
+datasets<a href="#fnref1" class="footnote-back"
+role="doc-backlink">↩︎</a></p></li>
+</ol>
+</section>
+
+</div>
+
+## Math & Reasoning
+
+|                 Model |   gsm8k (0-shot)    |    MATH (0-shot)    |    BBH (0-shot)     |
+|----------------------:|:-------------------:|:-------------------:|:-------------------:|
+|                 GPT-4 |        91.4         |        45.8         |        86.7         |
+|          SUS-Chat-34B |      **80.06**      |        28.7         |        67.62        |
+|         Qwen-72b-Chat | $\underline{76.57}$ |      **35.9**       |      **72.63**      |
+|     Deepseek-68b-Chat |        74.45        | $\underline{29.56}$ | $\underline{69.73}$ |
+| OrionStar-Yi-34B-Chat |        54.36        |        12.8         |        62.88        |
+|           Yi-34B-Chat |        63.76        |        10.02        |        61.54        |
+
+## More Tasks
+
+|                 Model | winogrande (5-shot) |    arc (25-shot)    | hellaswag (10-shot) | TruthfulQA mc1 (0-shot) | TruthfulQA mc2 (0-shot) |
+|----------------------:|:-------------------:|:-------------------:|:-------------------:|:-----------------------:|:-----------------------:|
+|                 GPT-4 |          —          |        94.5         |        91.4         |          59.00          |            —            |
+|          SUS-Chat-34B |      **81.22**      | $\underline{81.54}$ |        83.79        |        **40.64**        |        **57.47**        |
+|         Qwen-72b-Chat |        76.09        |      **82.10**      | $\underline{86.06}$ |          39.17          |   $\underline{56.37}$   |
+|     Deepseek-68b-Chat | $\underline{80.58}$ |        81.29        |      **87.02**      |   $\underline{40.02}$   |          50.64          |
+| OrionStar-Yi-34B-Chat |        77.27        |        80.19        |        84.54        |          36.47          |          53.24          |
+|           Yi-34B-Chat |        76.64        |        70.66        |        82.29        |          38.19          |          54.57          |
+
+## Overall
+
+|                 Model |  Average  |
+|----------------------:|:---------:|
+|          SUS-Chat-34B | **69.05** |
+|         Qwen-72b-Chat |   68.41   |
+|     Deepseek-68b-Chat |   62.91   |
+| OrionStar-Yi-34B-Chat |   60.21   |
+|           Yi-34B-Chat |   59.72   |
+
+To reproduce the results, please start a corresponding vllm server and
+refer to
+[here](https://sustech-tlem.static.hf.space/index.html#start-evaluating-your-model-in-3-line).
+
 # Usage
 
 SUS-Chat-34B is a standard LLaMA model and should be seamlessly
 compatible with the LLaMA ecosystem. We provide the following example to
 demonstrate how it can be used for multi-turn dialogues.
 
-``` python
-from transformers import AutoModelForCausalLM, AutoTokenizer
+Feel free to [open an
+issue](https://github.com/SUSTech-IDEA/SUS-Chat/issues) if you have any
+questions.
 
+``` python
+from transformers import AutoModelForCausalLM, AutoTokenizer # 🤗 Transformers, or 
+# from modelscope import AutoModelForCausalLM, AutoTokenizer # 🤖 ModelScope
 
 def chat_template(messages):
     history = ""
@@ -230,5 +399,5 @@ model.
 
 This model is developed entirely for academic research and free
 commercial use, but it must adhere to the
-[license](https://github.com/SUSTech-IDEA/SUS-Chat/blob/main/MODEL_LICENSE_AGREEMENT.txt)
-from 01-ai.
+[license](https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt)
+from [01-ai](https://huggingface.co/01-ai).