Edit model card

Duplicated from the below; modified for embedding usage

Hebrew-Mistral-7B

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.

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")
model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B")

input_text = "ืฉืœื•ื! ืžื” ืฉืœื•ืžืš ื”ื™ื•ื?"
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")
model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B", device_map="auto")

input_text = "ืฉืœื•ื! ืžื” ืฉืœื•ืžืš ื”ื™ื•ื?"
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")
model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B", quantization_config = BitsAndBytesConfig(load_in_4bit=True))

input_text = "ืฉืœื•ื! ืžื” ืฉืœื•ืžืš ื”ื™ื•ื?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0])

Notice

Hebrew-Mistral-7B is a pretrained base model and therefore does not have any moderation mechanisms.

Authors

  • Trained by Yam Peleg.
  • In collaboration with Jonathan Rouach and Arjeo, inc.
Downloads last month
14
Safetensors
Model size
7.5B params
Tensor type
BF16
ยท
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.