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README.md
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---
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tags:
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- llama2
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---
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## Model Details
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### Model Description
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We have followed up on our previous training runs related to extending the context length
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of Llama models. The associated github repository
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https://github.com/abacusai/long-context
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has some basic details on our approach and metrics. We have also published a paper on arXiv
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that covers our experiments and analysis a lot more comprehensively.
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http://arxiv.org/abs/2308.10882
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- **Developed by:** [Abacus.AI](https://abacus.ai)
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- **Model type:** Transformer based autoregressive causal language model
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- **License:** Llama 2 Community License: https://github.com/facebookresearch/llama/blob/main/LICENSE
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- **Finetuned from model:** Llama V2 70B
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### Usage
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To use this model at longer lengths the model needs to be patched to interpolate the longer context
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lengths. It will not work if it is simply loaded with the `AutoModel` framework of `transformers`.
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For full details and usage see:
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https://github.com/abacusai/Long-Context
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The evaluation section has detailed code for how to load and patch the model for inference (or further fine-tuning).
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Note in particular the `max_position_embeddings` is not relevant since the patched module dynamically reallocates
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the position buffers as required.
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The tokenizer corresponding to this model is https://huggingface.co/abacusai/Giraffe-v1-Tokenizer.
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Using the code in the repository you can load this model with the following code:
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```python
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from models import load_model, load_tokenizer
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tokenizer = load_tokenizer()
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model = load_model('abacusai/Giraffe-v2-70b-32k', scale=8)
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```
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