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Yi-34B-200K-4.65bpw-h6-exl2

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ExLLaMA V2 quant of Yi-34B-200K-4.65bpw-h6-exl2

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  1. README.md +94 -0
  2. config.json +29 -0
  3. generation_config.json +7 -0
  4. output-00001-of-00003.safetensors +3 -0
  5. output-00002-of-00003.safetensors +3 -0
  6. output-00003-of-00003.safetensors +3 -0
  7. pytorch_model.bin.index.json +550 -0
  8. tokenizer.model +3 -0
  9. tokenizer_config.json +9 -0
README.md ADDED
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+ ---
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+ license: other
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+ license_name: yi-license
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+ license_link: LICENSE
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+ ---
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+ <div align="center">
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+
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+ <img src="./Yi.svg" width="200px">
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+
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+ </div>
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+
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+ ## Introduction
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+
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+ The **Yi** series models are large language models trained from scratch by
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+ developers at [01.AI](https://01.ai/). The first public release contains two
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+ bilingual(English/Chinese) base models with the parameter sizes of 6B([`Yi-6B`](https://huggingface.co/01-ai/Yi-6B))
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+ and 34B([`Yi-34B`](https://huggingface.co/01-ai/Yi-34B)). Both of them are trained
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+ with 4K sequence length and can be extended to 32K during inference time.
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+ The [`Yi-6B-200K`](https://huggingface.co/01-ai/Yi-6B-200K)
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+ and [`Yi-34B-200K`](https://huggingface.co/01-ai/Yi-34B-200K) are base model with
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+ 200K context length.
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+
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+ ## News
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+
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+ - 🎯 **2023/11/06**: The base model of [`Yi-6B-200K`](https://huggingface.co/01-ai/Yi-6B-200K)
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+ and [`Yi-34B-200K`](https://huggingface.co/01-ai/Yi-34B-200K) with 200K context length.
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+ - 🎯 **2023/11/02**: The base model of [`Yi-6B`](https://huggingface.co/01-ai/Yi-6B) and
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+ [`Yi-34B`](https://huggingface.co/01-ai/Yi-34B).
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+
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+
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+ ## Model Performance
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+
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+ | Model | MMLU | CMMLU | C-Eval | GAOKAO | BBH | Common-sense Reasoning | Reading Comprehension | Math & Code |
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+ | :------------ | :------: | :------: | :------: | :------: | :------: | :--------------------: | :-------------------: | :---------: |
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+ | | 5-shot | 5-shot | 5-shot | 0-shot | 3-shot@1 | - | - | - |
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+ | LLaMA2-34B | 62.6 | - | - | - | 44.1 | 69.9 | 68.0 | 26.0 |
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+ | LLaMA2-70B | 68.9 | 53.3 | - | 49.8 | 51.2 | 71.9 | 69.4 | 36.8 |
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+ | Baichuan2-13B | 59.2 | 62.0 | 58.1 | 54.3 | 48.8 | 64.3 | 62.4 | 23.0 |
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+ | Qwen-14B | 66.3 | 71.0 | 72.1 | 62.5 | 53.4 | 73.3 | 72.5 | **39.8** |
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+ | Skywork-13B | 62.1 | 61.8 | 60.6 | 68.1 | 41.7 | 72.4 | 61.4 | 24.9 |
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+ | InternLM-20B | 62.1 | 59.0 | 58.8 | 45.5 | 52.5 | 78.3 | - | 30.4 |
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+ | Aquila-34B | 67.8 | 71.4 | 63.1 | - | - | - | - | - |
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+ | Falcon-180B | 70.4 | 58.0 | 57.8 | 59.0 | 54.0 | 77.3 | 68.8 | 34.0 |
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+ | Yi-6B | 63.2 | 75.5 | 72.0 | 72.2 | 42.8 | 72.3 | 68.7 | 19.8 |
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+ | Yi-6B-200K | 64.0 | 75.3 | 73.5 | 73.9 | 42.0 | 72.0 | 69.1 | 19.0 |
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+ | **Yi-34B** | **76.3** | **83.7** | 81.4 | 82.8 | **54.3** | **80.1** | 76.4 | 37.1 |
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+ | Yi-34B-200K | 76.1 | 83.6 | **81.9** | **83.4** | 52.7 | 79.7 | **76.6** | 36.3 |
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+
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+ While benchmarking open-source models, we have observed a disparity between the
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+ results generated by our pipeline and those reported in public sources (e.g.
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+ OpenCompass). Upon conducting a more in-depth investigation of this difference,
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+ we have discovered that various models may employ different prompts,
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+ post-processing strategies, and sampling techniques, potentially resulting in
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+ significant variations in the outcomes. Our prompt and post-processing strategy
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+ remains consistent with the original benchmark, and greedy decoding is employed
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+ during evaluation without any post-processing for the generated content. For
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+ scores that were not reported by the original authors (including scores reported
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+ with different settings), we try to get results with our pipeline.
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+
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+ To evaluate the model's capability extensively, we adopted the methodology
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+ outlined in Llama2. Specifically, we included PIQA, SIQA, HellaSwag, WinoGrande,
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+ ARC, OBQA, and CSQA to assess common sense reasoning. SquAD, QuAC, and BoolQ
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+ were incorporated to evaluate reading comprehension. CSQA was exclusively tested
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+ using a 7-shot setup, while all other tests were conducted with a 0-shot
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+ configuration. Additionally, we introduced GSM8K (8-shot@1), MATH (4-shot@1),
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+ HumanEval (0-shot@1), and MBPP (3-shot@1) under the category "Math & Code". Due
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+ to technical constraints, we did not test Falcon-180 on QuAC and OBQA; the score
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+ is derived by averaging the scores on the remaining tasks. Since the scores for
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+ these two tasks are generally lower than the average, we believe that
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+ Falcon-180B's performance was not underestimated.
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+
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+ ## Usage
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+
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+ Please visit our [github repository](https://github.com/01-ai/Yi) for general
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+ guidance on how to use this model.
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+
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+ ## Disclaimer
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+
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+ Although we use data compliance checking algorithms during the training process
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+ to ensure the compliance of the trained model to the best of our ability, due to
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+ the complexity of the data and the diversity of language model usage scenarios,
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+ we cannot guarantee that the model will generate correct and reasonable output
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+ in all scenarios. Please be aware that there is still a risk of the model
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+ producing problematic outputs. We will not be responsible for any risks and
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+ issues resulting from misuse, misguidance, illegal usage, and related
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+ misinformation, as well as any associated data security concerns.
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+
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+ ## License
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+
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+ The Yi series models are fully open for academic research and free commercial
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+ usage with permission via applications. All usage must adhere to the [Model
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+ License Agreement 2.0](https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE). To
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+ apply for the official commercial license, please contact us
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+ ([[email protected]](mailto:[email protected])).
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