Hebrew-Mistral-7B-200K
Please note: There has been some issues reported about this model, updates coming soon.
Hebrew-Mistral-7B-200K is an open-source Large Language Model (LLM) pretrained in hebrew and english pretrained with 7B billion parameters and with 200K context length, based on Mistral-7B-v1.0 from Mistral.
It has an extended hebrew tokenizer with 64,000 tokens and is continuesly pretrained from Mistral-7B on tokens in both English and Hebrew.
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.
Usage
Below are some code snippets on how to get quickly started with running the model.
First make sure to pip install -U transformers
, then copy the snippet from the section that is relevant for your usecase.
Running on CPU
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Mistral-7B-200K")
model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B-200K")
input_text = "ืฉืืื! ืื ืฉืืืื ืืืื?"
input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
Running on GPU
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Mistral-7B-200K")
model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B-200K", device_map="auto")
input_text = "ืฉืืื! ืื ืฉืืืื ืืืื?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
Running with 4-Bit precision
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Mistral-7B-200K")
model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B-200K", quantization_config = BitsAndBytesConfig(load_in_4bit=True))
input_text = "ืฉืืื! ืื ืฉืืืื ืืืื?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0])
Notice
Hebrew-Mistral-7B-200K is a pretrained base model and therefore does not have any moderation mechanisms.
Authors
- Trained by Yam Peleg.
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