ExLLaMA V2 quant of Yi-6B-200K-5.0bpw-h6-exl2

README.md

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+---
+license: other
+license_name: yi-license
+license_link: LICENSE
+---
+<div align="center">
+
+<img src="./Yi.svg" width="200px">
+
+</div>
+
+## Introduction
+
+The **Yi** series models are large language models trained from scratch by
+developers at [01.AI](https://01.ai/). The first public release contains two
+bilingual(English/Chinese) base models with the parameter sizes of 6B([`Yi-6B`](https://huggingface.co/01-ai/Yi-6B)) 
+and 34B([`Yi-34B`](https://huggingface.co/01-ai/Yi-34B)). Both of them are trained 
+with 4K sequence length and can be extended to 32K during inference time. 
+The [`Yi-6B-200K`](https://huggingface.co/01-ai/Yi-6B-200K)
+and [`Yi-34B-200K`](https://huggingface.co/01-ai/Yi-34B-200K) are base model with
+200K context length.
+
+## News
+
+- 🎯 **2023/11/06**: The base model of [`Yi-6B-200K`](https://huggingface.co/01-ai/Yi-6B-200K) 
+and [`Yi-34B-200K`](https://huggingface.co/01-ai/Yi-34B-200K) with 200K context length.
+- 🎯 **2023/11/02**: The base model of [`Yi-6B`](https://huggingface.co/01-ai/Yi-6B) and 
+[`Yi-34B`](https://huggingface.co/01-ai/Yi-34B).
+
+
+## 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
+
+Please visit our [github repository](https://github.com/01-ai/Yi) for general
+guidance on how to use this model.
+
+## Disclaimer
+
+Although 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
+the complexity of the 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 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://huggingface.co/01-ai/Yi-6B-200K/blob/main/LICENSE). To
+apply for the official commercial license, please contact us
+([yi@01.ai](mailto:yi@01.ai)).

config.json

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+{
+  "architectures": [
+    "YiForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_yi.YiConfig",
+    "AutoModel": "modeling_yi.YiModel",
+    "AutoModelForCausalLM":"modeling_yi.YiForCausalLM"
+    },
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 200000,
+  "model_type": "Yi",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 5000000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.34.0",
+  "use_cache": true,
+  "vocab_size": 64000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.31.0"
+}

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