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# webbigdata/ALMA-7B-Ja
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ALMA-7B-Ja is a machine translation model that uses ALMA's learning method to translate Japanese to English.(13.3GB)
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The original ALMA-7B supports English and Russian(ru) translation. This model supports Japanese(ja) and English translations instead of Russian.
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Like the original model, This model has been verified that it also has a translation ability between the following languages, but if you want the translation function for these languages, it is better to use the original [ALMA-13B model](https://huggingface.co/haoranxu/ALMA-13B).
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German(de) and English(en)
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Chinese(zh) and English(en)
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Icelandic(is) and English(en)
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Czech(cs) and English(en)
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Translating from English (en→xx) BLEU/COMET
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Models | de | cs | is | zh | ru/jp | Avg. |
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ALMA-7B(Original)| 30.26/84.00 | 43.91/85.86 | 35.97/86.03 | 23.75/79.85 | 39.37/84.58 | 34.55/84.02 |
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ALMA-7B-Ja(Ours) | 26.41/83.13 | 34.39/83.50 | 24.77/81.12 | 20.60/78.54 | 15.57/78.61 | 24.35/81.76 |
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If you want to translate the entire file at once, try Colab below.
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[ALMA_7B_Ja_GPTQ_Ja_En_batch_translation_sample](https://github.com/webbigdata-jp/python_sample/blob/main/ALMA_7B_Ja_GPTQ_Ja_En_batch_translation_sample.ipynb)
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There is also a GPTQ quantized version model that reduces model size(3.9GB) and memory usage, although the performance is probably lower.
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And translation ability for languages other than Japanese and English has deteriorated significantly.
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[webbigdata/ALMA-7B-Ja-GPTQ-Ja-En](https://huggingface.co/webbigdata/ALMA-7B-Ja-GPTQ-Ja-En)
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**ALMA** (**A**dvanced **L**anguage **M**odel-based tr**A**nslator) is an LLM-based translation model, which adopts a new translation model paradigm: it begins with fine-tuning on monolingual data and is further optimized using high-quality parallel data. This two-step fine-tuning process ensures strong translation performance.
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# webbigdata/ALMA-7B-Ja
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ALMA-7B-Ja(13.3GB) is a machine translation model that uses ALMA's learning method to translate Japanese to English.
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The [original ALMA-7B (26.95GB)](https://huggingface.co/haoranxu/ALMA-7B) supports English and Russian(ru) translation. This model supports Japanese(ja) and English translations instead of Russian.
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Like the original model, This model has been verified that it also has a translation ability between the following languages, but if you want the translation function for these languages, it is better to use the original [ALMA-13B model](https://huggingface.co/haoranxu/ALMA-13B).
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- German(de) and English(en)
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- Chinese(zh) and English(en)
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- Icelandic(is) and English(en)
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- Czech(cs) and English(en)
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Translating from English (en→xx) BLEU/COMET
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Models | de | cs | is | zh | ru/jp | Avg. |
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ALMA-7B(Original)| 30.26/84.00 | 43.91/85.86 | 35.97/86.03 | 23.75/79.85 | 39.37/84.58 | 34.55/84.02 |
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ALMA-7B-Ja(Ours) | 26.41/83.13 | 34.39/83.50 | 24.77/81.12 | 20.60/78.54 | 15.57/78.61 | 24.35/81.76 |
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[Sample Code For Free Colab](https://github.com/webbigdata-jp/python_sample/blob/main/ALMA_7B_Ja_Free_Colab_sample.ipynb)
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## Other Version
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### webbigdata-ALMA-7B-Ja-gguf
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mmnga made llama.cpp(gguf) version [webbigdata-ALMA-7B-Ja-gguf](https://huggingface.co/mmnga/webbigdata-ALMA-7B-Ja-gguf). Thank you!
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llama.cpp is a tool used primarily on Macs, and gguf is its latest version format. It can be used without gpu.
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### ALMA-7B-Ja-GPTQ-Ja-En
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GPTQ is quantized(reduce the size of the model) method and ALMA-7B-Ja-GPTQ has GPTQ quantized version that reduces model size(3.9GB) and memory usage.
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But the performance is probably lower. And translation ability for languages other than Japanese and English has deteriorated significantly.
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[Sample Code For Free Colab webbigdata/ALMA-7B-Ja-GPTQ-Ja-En](https://huggingface.co/webbigdata/ALMA-7B-Ja-GPTQ-Ja-En)
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If you want to translate the entire file at once, try Colab below.
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[ALMA_7B_Ja_GPTQ_Ja_En_batch_translation_sample](https://github.com/webbigdata-jp/python_sample/blob/main/ALMA_7B_Ja_GPTQ_Ja_En_batch_translation_sample.ipynb)
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**ALMA** (**A**dvanced **L**anguage **M**odel-based tr**A**nslator) is an LLM-based translation model, which adopts a new translation model paradigm: it begins with fine-tuning on monolingual data and is further optimized using high-quality parallel data. This two-step fine-tuning process ensures strong translation performance.
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