75 lines
2.4 KiB
Markdown
75 lines
2.4 KiB
Markdown
---
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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 = "שלום! מה שלומך היום?"
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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 = "שלום! מה שלומך היום?"
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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 = "שלום! מה שלומך היום?"
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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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