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license: mit
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license: mit
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<img src="https://l0d0v1c.github.io/notes/medias/greeks.jpg"/>
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# An Ancient Greek/French Model for RAG
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* based on mistralai/Mistral-7B-v0.1
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* Trained on [1] and [2]
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* For RAG [3]
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* may produce unaccurate results in translation
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* https://www.diogenial.com https://rd-mediation.com
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* Framework MLX Apple Silicon [4]
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# Example
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<code>La traduction de Φασὶ δὲ καὶ Ἀλέξανδρον εἰπεῖν ὡς εἴπερ Ἀλέξανδρος μὴ ἐγεγόνει, ἐθελῆσαι ἂν Διογένης γενέσθαι est:
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Diogène dit que si Alexandre avait été moins ambitieux, il aurait bien voulu être Diogène.</code>
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(imprécise: On assure qu’Alexandre disait que s’il n’était pas Alexandre il voudrait être Diogène. selon [5])
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# Use
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<code>from mlx_lm import load, generate
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model, tokenizer = load("RANDMEDIATION/DiogenialRAG")
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response = generate(model, tokenizer, prompt=input("prompt:"), verbose=True,max_tokens=256,temp=0.8)</code>
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# References
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[1] Giannantoni, Gabriele, éd. Socratis et Socraticorum reliquiae. Elenchos 18. Book V. Naples, Italy: Bibliopolis, 1990.
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[2] https://tatoeba.org/fr/sentences/show_all_in/grc/none
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[3] Lewis, Patrick, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, et al. « Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks ». In Advances in Neural Information Processing Systems, édité par H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, et H. Lin, 33:9459‑74. Curran Associates, Inc., 2020. https://proceedings.neurips.cc/paper_files/paper/2020/file/6b493230205f780e1bc26945df7481e5-Paper.pdf.
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[4] Hannun, Awni, Jagrit Digani, Angelos Katharopoulos, et Ronan Collobert. « MLX: Efficient and flexible machine learning on Apple silicon », 2023. https://github.com/ml-explore.
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[5] https://remacle.org/bloodwolf/philosophes/laerce/6diogene1.htm
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