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README.md
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---
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license: llama3
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base_model:
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- catallama/CataLlama-v0.2-Instruct-SFT
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- catallama/CataLlama-v0.2-Instruct-DPO
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tags:
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- llama
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- llama-3
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- catalan
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model-index:
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- name: CataLlama-v0.2-Instruct-SFT-DPO-Merged
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results: []
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datasets:
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- catallama/Catalan-DPO-V2
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- catallama/Catalan-Instruct-V2
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language:
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- ca
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- en
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pipeline_tag: text-generation
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---
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![](https://huggingface.co/catallama/CataLlama-v0.2-Instruct-SFT/resolve/main/CataLlama-v0.2.png)
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# CataLlama-v0.2-Instruct-SFT-DPO-Merged-GGUF
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**CataLlama-v0.2-Instruct-SFT-DPO-Merged** is a merge between [catallama/CataLlama-v0.2-Instruct-SFT](https://huggingface.co/catallama/CataLlama-v0.2-Instruct-SFT) and [catallama/CataLlama-v0.2-Instruct-DPO](https://huggingface.co/catallama/CataLlama-v0.2-Instruct-DPO)
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The resulting model scores better than it's parents on both MMLU and GSM8K.
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**This is an instruction fine-tuned model, optimised with DPO, proficient on the following tasks in Catalan**
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- *Information extraction (suitable for RAG)*
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- *Named Entity Recognition (NER)*
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- *Translation from English to Catalan and Catalan to English*
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- *Summarization - both short form and long form*
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- *Sentiment analysis*
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- *Chat*
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**Model developers** [Laurentiu Petrea](https://www.linkedin.com/in/laurentiupetrea/) based on Llama-3 from Meta.
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**Model Architecture** CataLlama is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and direct preference optimisation (DPO) to align with human preferences for helpfulness and safety.
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**License** The model uses the llama-3 license available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
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## Benchmarks
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| Model | CataLlama-v0.2-Instruct-DPO | CataLlama-v0.2-Instruct-SFT | CataLlama-v0.2-Instruct-SFT-DPO-Merged |
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| ------------------ | --------------------------- | ------------------------------- | ------------------------------------------ |
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| MMLU 5 shot | 58.89 | 59.35 | **60.53** |
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| GSM8K CoT 8 shot | 60.05 | 76.04 | **77.26** |
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## Merging procedure
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The merge was performed between the 32 layers of the two models, excluding the embedding, norm and the head layers.
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The weights of the 32 layers were merged in equal proportion simply by calculating the average of the corresponding weights from the parent models.
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The embedding, norm and head layers are copied from CataLlama-v0.2-Instruct-DPO without modification.
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**This was done with a custom script, without mergekit.**
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## Intended Use
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**Note:** This model is not intended to beat benchmarks, but to demonstrate techniques for augmenting LLMs on new languages and preserve rare languages as part of our world heritage.
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**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
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**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
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**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
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