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--- |
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license: apache-2.0 |
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language: |
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- en |
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- he |
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library_name: transformers |
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--- |
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# Hebrew-Mistral-7B |
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Hebrew-Mistral-7B is an open-source Large Language Model (LLM) pretrained in hebrew and english pretrained with 7B billion parameters, based on Mistral-7B-v1.0 from Mistral. |
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It has an extended hebrew tokenizer with 64,000 tokens and is continuesly pretrained from Mistral-7B on tokens in both English and Hebrew. |
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The resulting model is a powerful general-purpose language model suitable for a wide range of natural language processing tasks, with a focus on Hebrew language understanding and generation. |
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### Usage |
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Below are some code snippets on how to get quickly started with running the model. |
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First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. |
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### Running on CPU |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Mistral-7B") |
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model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B") |
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input_text = "ืฉืืื! ืื ืฉืืืื ืืืื?" |
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input_ids = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**input_ids) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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### Running on GPU |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Mistral-7B") |
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model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B", device_map="auto") |
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input_text = "ืฉืืื! ืื ืฉืืืื ืืืื?" |
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input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") |
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outputs = model.generate(**input_ids) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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### Running with 4-Bit precision |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Mistral-7B") |
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model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B", quantization_config = BitsAndBytesConfig(load_in_4bit=True)) |
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input_text = "ืฉืืื! ืื ืฉืืืื ืืืื?" |
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input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") |
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outputs = model.generate(**input_ids) |
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print(tokenizer.decode(outputs[0]) |
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``` |
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### Notice |
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Hebrew-Mistral-7B is a pretrained base model and therefore does not have any moderation mechanisms. |
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### Authors |
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- Trained by Yam Peleg. |
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- In collaboration with Jonathan Rouach and Arjeo, inc. |
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