Sync README

README.md

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+<div align="center">
+<p align="center">
+<img src="https://github.com/01-ai/Yi/raw/main/assets/img/Yi.svg?sanitize=true" width="200px">
+</p>
+<a href="https://github.com/01-ai/Yi/actions/workflows/ci.yml">
+  <img src="https://github.com/01-ai/Yi/actions/workflows/ci.yml/badge.svg">
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+<a href="https://huggingface.co/01-ai">
+  <img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-01--ai-blue">
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+<a href="https://www.modelscope.cn/organization/01ai/">
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+<a href="https://github.com/01-ai/Yi/blob/main/LICENSE">
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+</a>
+<a 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">
+</a>
+<a href="mailto:oss@01.ai">
+  <img src="https://img.shields.io/badge/✉️-yi@01.ai-FFE01B">
+</a>
+</div>
+
+## Introduction
+
+The **Yi** series models are large language models trained from scratch by
+developers at [01.AI](https://01.ai/).
+
+## News
+
+<details open>
+<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
+
+
+| 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.
+
+## Usage
+
+Feel free to [create an issue](https://github.com/01-ai/Yi/issues/new) if you
+encounter any problem when using the **Yi** series models.
+
+### 1. Prepare development environment
+
+The best approach to try the **Yi** series models is through Docker with GPUs. We
+provide the following docker images to help you get started.
+
+- `registry.lingyiwanwu.com/ci/01-ai/yi:latest`
+
+Note that the `latest` tag always points to the latest code in the `main`
+branch. To test a stable version, please replace it with a specific
+[tag](https://github.com/01-ai/Yi/tags).
+
+If you prefer to try out with your local development environment. First, create
+a virtual environment and clone this repo. Then install the dependencies with
+`pip install -r requirements.txt`. For the best performance, we recommend you
+also install the latest version (`>=2.3.3`) of
+[flash-attention](https://github.com/Dao-AILab/flash-attention#installation-and-features).
+
+### 2. Download the model (optional)
+
+By default, the model weights and tokenizer will be downloaded from
+[HuggingFace](https://huggingface.co/01-ai) automatically in the next step. You
+can also download them manually from the following places:
+
+- [ModelScope](https://www.modelscope.cn/organization/01ai/)
+- [WiseModel](https://wisemodel.cn/models) (Search for `Yi`)
+- Mirror site (remember to extract the content with `tar`)
+  - [Yi-6B.tar](https://storage.lingyiwanwu.com/yi/models/Yi-6B.tar)
+  - [Yi-6B-200K.tar](https://storage.lingyiwanwu.com/yi/models/Yi-6B-200K.tar)
+  - [Yi-34B.tar](https://storage.lingyiwanwu.com/yi/models/Yi-34B.tar)
+  - [Yi-34B-200K.tar](https://storage.lingyiwanwu.com/yi/models/Yi-34B-200K.tar)
+
+### 3. Examples
+
+#### 3.1 Use the base model
+
+```bash
+python demo/text_generation.py
+```
+
+To reuse the downloaded models in the previous step, you can provide the extra
+`--model` argument:
+
+```bash
+python demo/text_generation.py  --model /path/to/model
+```
+
+Or if you'd like to get your hands dirty:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-34B", device_map="auto", torch_dtype="auto", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-34B", trust_remote_code=True)
+inputs = tokenizer("There's a place where time stands still. A place of breath taking wonder, but also", return_tensors="pt")
+max_length = 256
+
+outputs = model.generate(
+    inputs.input_ids.cuda(),
+    max_length=max_length,
+    eos_token_id=tokenizer.eos_token_id,
+    do_sample=True,
+    repetition_penalty=1.3,
+    no_repeat_ngram_size=5,
+    temperature=0.7,
+    top_k=40,
+    top_p=0.8,
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+<details>
+
+<summary>Output</summary>
+
+**Prompt**: There's a place where time stands still. A place of breath taking wonder, but also
+
+**Generation**: There's a place where time stands still. A place of breath taking wonder, but also of great danger. A place where the very air you breathe could kill you. A place where the only way to survive is to be prepared.
+The place is called the Arctic.
+The Arctic is a vast, frozen wilderness. It is a place of extremes. The temperatures can drop to -40 degrees Celsius. The winds can reach speeds of 100 kilometers per hour. The sun can shine for 24 hours a day, or not at all for weeks on end.
+The Arctic is also a place of great beauty. The ice and snow are a pristine white. The sky is a deep blue. The sunsets are spectacular.
+But the Arctic is also a place of great danger. The ice can be treacherous. The winds can be deadly. The sun can be blinding.
+The Arctic is a place where the only way to survive is to be prepared.
+The Arctic is a place of extremes. The temperatures can drop to -40 degrees Celsius. The winds can reach speeds of 100 kilometers per hour. The sun can shine for 24 hours a day, or not at all for weeks on end.
+The Arctic is a place of great beauty. The ice and snow are a
+
+</details>
+
+For more advanced usage, please refer to the
+[doc](https://github.com/01-ai/Yi/tree/main/demo).
+
+#### 3.2 Finetuning from the base model:
+
+```bash
+bash finetune/scripts/run_sft_Yi_6b.sh
+```
+
+Once finished, you can compare the finetuned model and the base model with the following command:
+
+```bash
+bash finetune/scripts/run_eval.sh
+```
+
+For more advanced usage like fine-tuning based on your custom data, please refer
+the [doc](https://github.com/01-ai/Yi/tree/main/finetune).
+
+#### 3.3 Quantization
+
+##### GPT-Q
+```bash
+python quantization/gptq/quant_autogptq.py \
+  --model /base_model                      \
+  --output_dir /quantized_model            \
+  --trust_remote_code
+```
+
+Once finished, you can then evaluate the resulting model as follows:
+
+```bash
+python quantization/gptq/eval_quantized_model.py \
+  --model /quantized_model                       \
+  --trust_remote_code
+```
+
+For a more detailed explanation, please read the [doc](https://github.com/01-ai/Yi/tree/main/quantization/gptq)
+
+##### AWQ
+```bash
+python quantization/awq/quant_autoawq.py \
+  --model /base_model                      \
+  --output_dir /quantized_model            \
+  --trust_remote_code
+```
+
+Once finished, you can then evaluate the resulted model as follows:
+
+```bash
+python quantization/awq/eval_quantized_model.py \
+  --model /quantized_model                       \
+  --trust_remote_code
+```
+
+For more detailed explanation, please read the [doc](https://github.com/01-ai/Yi/tree/main/quantization/awq)
+
+## Disclaimer
+
+We use data compliance checking algorithms during the training process, to
+ensure the compliance of the trained model to the best of our ability. Due to
+complex data and the diversity of language model usage scenarios, we cannot
+guarantee that the model will generate correct, and reasonable output in all
+scenarios. Please be aware that there is still a risk of the model producing
+problematic outputs. We will not be responsible for any risks and issues
+resulting from misuse, misguidance, illegal usage, and related misinformation,
+as well as any associated data security concerns.
+
+## License
+
+The source code in this repo is licensed under the [Apache 2.0
+license](https://github.com/01-ai/Yi/blob/main/LICENSE). The Yi series models
+are fully open for academic research and free commercial usage with permission
+via applications. All usage must adhere to the [Model License
+Agreement 2.0](https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt).
+To apply for the official commercial license, please contact us
+([yi@01.ai](mailto:yi@01.ai)).