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ExLLaMA V2 quant of Yarn-Mistral-7b-128k-8.0bpw-h8-exl2

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README.md ADDED
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+ ---
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+ datasets:
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+ - emozilla/yarn-train-tokenized-16k-mistral
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+ metrics:
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+ - perplexity
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+ library_name: transformers
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+ ---
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+
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+ # Model Card: Nous-Yarn-Mistral-7b-128k
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+
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+ [Preprint (arXiv)](https://arxiv.org/abs/2309.00071)
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+ [GitHub](https://github.com/jquesnelle/yarn)
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+ ![yarn](https://raw.githubusercontent.com/jquesnelle/yarn/mistral/data/proofpile-long-small-mistral.csv.png)
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+
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+ ## Model Description
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+
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+ Nous-Yarn-Mistral-7b-128k is a state-of-the-art language model for long context, further pretrained on long context data for 1500 steps using the YaRN extension method.
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+ It is an extension of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and supports a 128k token context window.
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+
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+ To use, pass `trust_remote_code=True` when loading the model, for example
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+
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+ ```python
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+ model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Mistral-7b-128k",
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+ use_flash_attention_2=True,
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+ torch_dtype=torch.bfloat16,
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+ device_map="auto",
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+ trust_remote_code=True)
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+ ```
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+
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+ In addition you will need to use the latest version of `transformers` (until 4.35 comes out)
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+ ```sh
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+ pip install git+https://github.com/huggingface/transformers
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+ ```
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+
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+ ## Benchmarks
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+
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+ Long context benchmarks:
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+ | Model | Context Window | 8k PPL | 16k PPL | 32k PPL | 64k PPL | 128k PPL |
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+ |-------|---------------:|------:|----------:|-----:|-----:|------------:|
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+ | [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 8k | 2.96 | - | - | - | - |
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+ | [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) | 64k | 3.04 | 2.65 | 2.44 | 2.20 | - |
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+ | [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) | 128k | 3.08 | 2.68 | 2.47 | 2.24 | 2.19 |
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+
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+ Short context benchmarks showing that quality degradation is minimal:
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+ | Model | Context Window | ARC-c | Hellaswag | MMLU | Truthful QA |
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+ |-------|---------------:|------:|----------:|-----:|------------:|
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+ | [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 8k | 59.98 | 83.31 | 64.16 | 42.15 |
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+ | [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) | 64k | 59.38 | 81.21 | 61.32 | 42.50 |
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+ | [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) | 128k | 58.87 | 80.58 | 60.64 | 42.46 |
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+
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+ ## Collaborators
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+
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+ - [bloc97](https://github.com/bloc97): Methods, paper and evals
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+ - [@theemozilla](https://twitter.com/theemozilla): Methods, paper, model training, and evals
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+ - [@EnricoShippole](https://twitter.com/EnricoShippole): Model training
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+ - [honglu2875](https://github.com/honglu2875): Paper and evals
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+
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+ The authors would like to thank LAION AI for their support of compute for this model.
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+ It was trained on the [JUWELS](https://www.fz-juelich.de/en/ias/jsc/systems/supercomputers/juwels) supercomputer.
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