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Model: SUSTech/SUS-Chat-34B
Source: Original Platform

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README.md

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+---
+widget:
+- example_title: SUS-Chat
+  text: hi
+  output:
+    text: ' Hello! How can I assist you today?'
+pipeline_tag: text-generation
+license: apache-2.0
+---
+# 🐷SUS-Chat: Instruction tuning done right
+
+<p align="left">
+<a href="README_CN.md">中文</a>&nbsp ｜ &nbspEnglish&nbsp
+</p>
+
+<br><br>
+
+<div align="center">
+
+<p align="center">
+<img src="https://github.com/SUSTech-IDEA/SUS-Chat/raw/main/assets/sustech.svg?sanitize=true" width="200px">
+<img src="https://github.com/SUSTech-IDEA/SUS-Chat/raw/main/assets/ccnl.png?sanitize=true" width="200px">
+</p>
+
+<div style="display: inline-block;">
+
+<a rel="noopener nofollow" href="https://github.com/SUSTech-IDEA/SUS-Chat/issues">
+<img src="https://img.shields.io/github/issues/SUSTech-IDEA/SUS-Chat?logo=github" style="margin: 0 0;">
+</a>
+
+</div>
+
+<div style="display: inline-block;">
+
+<a href="https://huggingface.co/SUSTech">
+<img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-SUSTech-blue" style="margin: 0 0;">
+</a>
+
+</div>
+
+<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;">
+</a>
+
+</div>
+
+<a href="https://wisemodel.cn/organization/SUSTech">
+<img src="https://img.shields.io/badge/WiseModel-SUSTech-blue"> </a>
+
+<div style="display: inline-block;">
+
+<a rel="noopener nofollow" href="https://github.com/SUSTech-IDEA/SUS-Chat/blob/main/LICENSE">
+<img src="https://img.shields.io/badge/Code_License-Apache_2.0-lightblue" style="margin: 0 0;">
+</a>
+
+</div>
+
+<div style="display: inline-block;">
+
+<a rel="noopener nofollow" href="https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt">
+<img src="https://img.shields.io/badge/Model_License-Model_Agreement-lightblue" style="margin: 0 0;">
+</a>
+
+</div>
+
+<div style="display: inline-block;">
+
+<a rel="noopener nofollow" href="mailto:oss@data.sustech.edu.cn">
+<img src="https://img.shields.io/badge/✉️-data@sustech.edu.cn-FFE01B" style="margin: 0 0;">
+</a>
+
+</div>
+
+</div>
+
+# News
+
+- 2024-1-04: 🔥 `cloudyu` created a series of top ranked
+  [MOE](https://huggingface.co/cloudyu/Yi-34Bx2-MoE-60B) based on our
+  model
+
+- 2023-12-09: 🔥 `Tigerbot` variant has been
+  [deleted](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/438),
+  `SUS-Chat-34B` is now the the top-ranked LLaMA model and the
+  top-ranked chat model.
+
+- 2023-12-07: SUS-Chat-34B is now available on
+  [WiseModel🧠](https://wisemodel.cn/model/SUSTech/SUS-Chat-34B).
+
+- 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 available on
+  [HuggingFace🤗](https://huggingface.co/SUSTech/SUS-Chat-34B).
+
+# 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-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 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
+models. This bridges the gap between academia and industry in large
+language models and opens new possibilities for collaboration between
+academic and industrial sectors.
+
+# Performance
+
+To better evaluate the performance of the SUS-Chat-34B model, we
+conducted assessments across multiple benchmark tests and have
+open-sourced the evaluation framework
+[TLEM](https://huggingface.co/spaces/SUSTech/tlem) to facilitate
+replication and comparison by other researchers.
+
+In TLEM, we utilized various benchmark tests including MMLU, CMMLU,
+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
+susceptibility to illusions.
+
+Overall, the SUS-Chat-34B model significantly outperformed models of
+similar scale and achieved the most advanced comprehensive performance.
+
+<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;"><u>74.35</u></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;"><u>77.02</u></td>
+<td style="text-align: center;"><u>77.22</u></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 |  <u>76.57</u>  |   **35.9**    |  **72.63**   |
+|     Deepseek-68b-Chat |     74.45      | <u>29.56</u>  | <u>69.73</u> |
+| 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**      | <u>81.54</u>  |        83.79        |        **40.64**        |        **57.47**        |
+|         Qwen-72b-Chat |        76.09        |   **82.10**   |    <u>86.06</u>     |          39.17          |      <u>56.37</u>       |
+|     Deepseek-68b-Chat |    <u>80.58</u>     |     81.29     |      **87.02**      |      <u>40.02</u>       |          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.
+
+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 = ""
+    for message in messages:
+        match message:
+            case {"role": "user", "content": message}:
+                history += f"### Human: {message}\n\n### Assistant: "
+            case {"role": "assistant", "content": message}:
+                history += message
+    return history
+
+
+model_path = "SUSTech/SUS-Chat-34B"
+# model_path = "SUSTC/SUS-Chat-34B" # ModelScope
+
+tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
+model = AutoModelForCausalLM.from_pretrained(
+    model_path, device_map="auto", torch_dtype="auto"
+).eval()
+
+messages = [{"role": "user", "content": "hi"}]
+
+input_ids = tokenizer.encode(
+    chat_template(messages), return_tensors="pt", add_special_tokens=False
+).to("cuda")
+output_ids = model.generate(input_ids.to("cuda"), max_length=256)
+response = tokenizer.decode(
+    output_ids[0][input_ids.shape[1] :], skip_special_tokens=False
+)
+
+messages.append({"role": "assistant", "content": response})
+
+# Second round
+
+messages.append({"role": "user", "content": "What is the capital of China?"})
+
+input_ids = tokenizer.encode(
+    chat_template(messages), return_tensors="pt", add_special_tokens=False
+).to("cuda")
+output_ids = model.generate(input_ids.to("cuda"), max_length=256)
+response = tokenizer.decode(
+    output_ids[0][input_ids.shape[1] :], skip_special_tokens=False
+)
+
+messages.append({"role": "assistant", "content": response})
+```
+
+# Limitations
+
+SUS-Chat has only undergone supervised fine-tuning and has not yet been
+trained on human preference learning. As a result, it may produce
+unreasonable responses in some situations and exacerbate existing issues
+in language models, including hallucinations, non-determinism, and
+cumulative errors. To achieve better performance for downstream tasks,
+we recommend adjusting the generation configuration parameters
+accordingly.
+
+# Disclaimer
+
+During the training process, we used data compliance check algorithms to
+ensure the compliance of the training model as much as possible. Due to
+the complexity of the data and the diverse use cases of language models,
+we cannot guarantee that the model will produce correct and reasonable
+outputs in all scenarios. Please be aware that there is still a risk of
+the model generating problematic outputs. We will not be responsible for
+any risks or issues arising from misuse, misguidance, illegal use, and
+related misinformation, as well as data security issues related to the
+model.
+
+# License
+
+This model is developed entirely for academic research and free
+commercial use, but it must adhere to the
+[license](https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt)
+from [01-ai](https://huggingface.co/01-ai).

config.json

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+{
+  "_name_or_path": "01-ai/Yi-34B",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 7168,
+  "initializer_range": 0.02,
+  "intermediate_size": 20480,
+  "max_position_embeddings": 8192,
+  "model_type": "llama",
+  "num_attention_heads": 56,
+  "num_hidden_layers": 60,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 5000000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.35.0",
+  "use_cache": true,
+  "vocab_size": 64000
+}

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