Create README.md

README.md

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+---
+metrics:
+- perplexity
+library_name: transformers
+license: apache-2.0
+language:
+- en
+---
+
+# Model Card: Nous-Yarn-Llama-2-70b-32k
+
+[Preprint (arXiv)](https://arxiv.org/abs/2309.00071)  
+[GitHub](https://github.com/jquesnelle/yarn)
+![yarn](https://raw.githubusercontent.com/jquesnelle/yarn/70b/data/proofpile-long-small-32k-70b.csv.png)
+
+## Model Description
+
+Nous-Yarn-Llama-2-70b-32k is a state-of-the-art language model for long context, further pretrained on long context data for 400 steps using the YaRN extension method.
+It is an extension of [Llama-2-70b-hf](meta-llama/Llama-2-70b-hf) and supports a 32k token context window.
+
+To use, pass `trust_remote_code=True` when loading the model, for example
+
+```python
+model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Llama-2-70b-32k",
+  use_flash_attention_2=True,
+  torch_dtype=torch.bfloat16,
+  device_map="auto",
+  trust_remote_code=True)
+```
+
+In addition you will need to use the latest version of `transformers` (until 4.35 comes out)
+```sh
+pip install git+https://github.com/huggingface/transformers
+```
+
+## Benchmarks
+
+Long context benchmarks:
+| Model | Context Window | 1k PPL | 2k PPL | 4k PPL | 8k PPL | 16k PPL | 32k PPL |
+|-------|---------------:|-------:|--------:|------:|-------:|--------:|--------:|
+| [Llama-2-70b-hf](meta-llama/Llama-2-70b-hf) | 4k | - | - | - | - | - | - |
+| [Yarn-Llama-2-70b-32k](https://huggingface.co/NousResearch/Yarn-Llama-2-70b-32k) | 32k | - | - | - | - | - | - |
+
+Short context benchmarks showing that quality degradation is minimal:
+| Model | Context Window | ARC-c | Hellaswag | MMLU | Truthful QA |
+|-------|---------------:|------:|----------:|-----:|------------:|
+| [Llama-2-70b-hf](meta-llama/Llama-2-70b-hf) | 4k | - | - | - | - |
+| [Yarn-Llama-2-70b-32k](https://huggingface.co/NousResearch/Yarn-Llama-2-70b-32k) | 32k | - | - | - | - |
+
+## Collaborators
+
+ - [bloc97](https://github.com/bloc97): Methods, paper and evals
+ - [@theemozilla](https://twitter.com/theemozilla): Methods, paper, model training, and evals
+ - [@EnricoShippole](https://twitter.com/EnricoShippole): Model training
+ - [honglu2875](https://github.com/honglu2875): Paper and evals
+
+The authors would like to thank LAION AI for their support of compute for this model.
+It was trained on the [JUWELS](https://www.fz-juelich.de/en/ias/jsc/systems/supercomputers/juwels) supercomputer.