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---
license: other
license_name: yi-license
license_link: LICENSE
widget:
  - example_title: Yi-34B-Chat
    text: hi
    output:
      text: ' Hello! How can I assist you today?'
  - example_title: Yi-34B
    text: >-
      There's a place where time stands still. A place of breath taking wonder,
      but also
    output:
      text: >2-
         an eerie sense that something is just not right…
        Between the two worlds lies The Forgotten Kingdom - home to creatures
        long since thought extinct and ancient magic so strong it defies belief!
        Only here can you find what has been lost for centuries: An Elixir Of
        Life which will restore youth and vitality if only those who seek its
        power are brave enough to face up against all manner of dangers lurking
        in this mysterious land! But beware; some say there may even exist
        powerful entities beyond our comprehension whose intentions towards
        humanity remain unclear at best ---- they might want nothing more than
        destruction itself rather then anything else from their quest after
        immortality (and maybe someone should tell them about modern medicine)?
        In any event though  one thing remains true regardless : whether or not
        success comes easy depends entirely upon how much effort we put into
        conquering whatever challenges lie ahead along with having faith deep
        down inside ourselves too ;) So let’s get started now shall We?
pipeline_tag: text-generation
---

.

## Introduction

**SUS-CHhat** is powered by SUSTech x IDEA-CCNL, based on `01-ai/Yi-34B`

## News

<details open>
<summary>🎯 <b>2023/11/23</b>: The chat models are open to public.</summary>

This release contains two chat models based on previous released base models, two 8-bits models quantized by GPTQ, two 4-bits models quantized by AWQ.

- `Yi-34B-Chat`
- `Yi-34B-Chat-4bits`
- `Yi-34B-Chat-8bits`
- `Yi-6B-Chat`
- `Yi-6B-Chat-4bits`
- `Yi-6B-Chat-8bits`

You can try some of them interactively at:

- [HuggingFace](https://huggingface.co/spaces/01-ai/Yi-34B-Chat)
- [Replicate](https://replicate.com/01-ai)
</details>

<details open>
<summary>🔔 <b>2023/11/23</b>: The Yi Series Models Community License Agreement is updated to v2.1.</summary>
</details>

<details>
<summary>🔥 <b>2023/11/08</b>: Invited test of Yi-34B chat model.</summary>

Application form:

- [English](https://cn.mikecrm.com/l91ODJf)
- [Chinese](https://cn.mikecrm.com/gnEZjiQ)

</details>

<details>
<summary>🎯 <b>2023/11/05</b>: The base model of <code>Yi-6B-200K</code> and <code>Yi-34B-200K</code>.</summary>

This release contains two base models with the same parameter sizes of previous
release, except that the context window is extended to 200K.

</details>

<details>
<summary>🎯 <b>2023/11/02</b>: The base model of <code>Yi-6B</code> and <code>Yi-34B</code>.</summary>

The first public release contains two bilingual (English/Chinese) base models
with the parameter sizes of 6B and 34B.  Both of them are trained with 4K
sequence length and can be extended to 32K during inference time.

</details>

## Model Performance

### Base Model Performance

| Model         |   MMLU   |  CMMLU   |  C-Eval  |  GAOKAO  |   BBH    | Common-sense Reasoning | Reading Comprehension | Math & Code |
| :------------ | :------: | :------: | :------: | :------: | :------: | :--------------------: | :-------------------: | :---------: |
|               |  5-shot  |  5-shot  |  5-shot  |  0-shot  | 3-shot@1 |           -            |           -           |      -      |
| LLaMA2-34B    |   62.6   |    -     |    -     |    -     |   44.1   |          69.9          |         68.0          |    26.0     |
| LLaMA2-70B    |   68.9   |   53.3   |    -     |   49.8   |   51.2   |          71.9          |         69.4          |    36.8     |
| Baichuan2-13B |   59.2   |   62.0   |   58.1   |   54.3   |   48.8   |          64.3          |         62.4          |    23.0     |
| Qwen-14B      |   66.3   |   71.0   |   72.1   |   62.5   |   53.4   |          73.3          |         72.5          |  **39.8**   |
| Skywork-13B   |   62.1   |   61.8   |   60.6   |   68.1   |   41.7   |          72.4          |         61.4          |    24.9     |
| InternLM-20B  |   62.1   |   59.0   |   58.8   |   45.5   |   52.5   |          78.3          |           -           |    30.4     |
| Aquila-34B    |   67.8   |   71.4   |   63.1   |    -     |    -     |           -            |           -           |      -      |
| Falcon-180B   |   70.4   |   58.0   |   57.8   |   59.0   |   54.0   |          77.3          |         68.8          |    34.0     |
| Yi-6B         |   63.2   |   75.5   |   72.0   |   72.2   |   42.8   |          72.3          |         68.7          |    19.8     |
| Yi-6B-200K    |   64.0   |   75.3   |   73.5   |   73.9   |   42.0   |          72.0          |         69.1          |    19.0     |
| **Yi-34B**    | **76.3** | **83.7** |   81.4   |   82.8   | **54.3** |        **80.1**        |         76.4          |    37.1     |
| Yi-34B-200K   |   76.1   |   83.6   | **81.9** | **83.4** |   52.7   |          79.7          |       **76.6**        |    36.3     |

While benchmarking open-source models, we have observed a disparity between the
results generated by our pipeline and those reported in public sources (e.g.
OpenCompass). Upon conducting a more in-depth investigation of this difference,
we have discovered that various models may employ different prompts,
post-processing strategies, and sampling techniques, potentially resulting in
significant variations in the outcomes. Our prompt and post-processing strategy
remains consistent with the original benchmark, and greedy decoding is employed
during evaluation without any post-processing for the generated content. For
scores that were not reported by the original authors (including scores reported
with different settings), we try to get results with our pipeline.

To evaluate the model's capability extensively, we adopted the methodology
outlined in Llama2. Specifically, we included PIQA, SIQA, HellaSwag, WinoGrande,
ARC, OBQA, and CSQA to assess common sense reasoning. SquAD, QuAC, and BoolQ
were incorporated to evaluate reading comprehension. CSQA was exclusively tested
using a 7-shot setup, while all other tests were conducted with a 0-shot
configuration. Additionally, we introduced GSM8K (8-shot@1), MATH (4-shot@1),
HumanEval (0-shot@1), and MBPP (3-shot@1) under the category "Math & Code". Due
to technical constraints, we did not test Falcon-180 on QuAC and OBQA; the score
is derived by averaging the scores on the remaining tasks. Since the scores for
these two tasks are generally lower than the average, we believe that
Falcon-180B's performance was not underestimated.