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--- |
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language: multilingual |
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license: mit |
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base_model: |
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- facebook/m2m100-12B-avg-5-ckpt |
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--- |
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# M2M100 12B (average of last 5 checkpoints) |
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- This is a copy of the model repository facebook/m2m100-12B-avg-5-ckpt, |
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"a multilingual encoder-decoder (seq-to-seq) model trained for |
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Many-to-Many multilingual translation". |
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- The model in the original repository is a single file of size 47.2 GB |
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which can be an issue for people behind proxies where downloading |
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files greater than xxGB is not permitted. |
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Steps: |
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- The model weights have been converted to `bfloat16`. |
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- The model file has been chunked into files no greater than 5 GB. |
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## Usage |
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Sample usage: |
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```python |
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from transformers import M2M100Tokenizer, M2M100ForConditionalGeneration |
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from threading import Lock |
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model_name = 'Didier/m2m100-12B-avg-5-ckpt' |
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device = 'mps' # if on Apple silicon |
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tokenizer = M2M100Tokenizer.from_pretrained(model_name) |
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model = M2M100ForConditionalGeneration.from_pretrained( |
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model_name, device_map=device, low_cpu_mem_usage=True) |
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lock = Lock() |
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def translate(text: str, src_lang: str, tgt_lang: str) -> str: |
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# Acquire lock to set src_lang and tokenize atomically |
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with lock: |
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tokenizer.src_lang = src_lang |
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input_ids = tokenizer([text,], return_tensors="pt").input_ids.to(model.device) |
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# Generate translation (outside the lock to allow parallel model |
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outputs = model.generate( |
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input_ids=input_ids, |
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forced_bos_token_id=tokenizer.get_lang_id(tgt_lang)) |
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translation = tokenizer.batch_decode( |
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outputs, skip_special_tokens=True)[0] |
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return translation |
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text = "ist der Ruf erst ruiniert, lebt es sich ganz ungeniert." |
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src_lang = 'de' |
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tgt_lang = 'en' |
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translation = translate(text, src_lang, tgt_lang) |
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print(f"{translation=}") |
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# --> "Once your reputation is ruined, you can live quite freely." |
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``` |
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