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Hebrew-Gemma-11B-V2

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metadata
license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
language:
  - en
  - he
library_name: transformers

Hebrew-Gemma-11B-V2

An updated version of Hebrew-Gemma-11B that was trained longer and had some bugs fixes.

Base Models:

Instruct Models:

Hebrew-Gemma-11B is an open-source Large Language Model (LLM) is a hebrew/english pretrained generative text model with 11 billion parameters, based on the Gemma-7B architecture from Google.

It is continued pretrain of gemma-7b, extended to a larger scale and trained on 3B additional tokens of both English and Hebrew text data.

The resulting model Gemma-11B 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.

Terms of Use

As an extention of Gemma-7B, this model is subject to the original license and terms of use by Google.

Gemma-7B original Terms of Use: Terms

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-Gemma-11B-V2")
model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Gemma-11B-V2")

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-Gemma-11B-V2")
model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Gemma-11B-V2", 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-Gemma-11B-V2")
model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Gemma-11B-V2", 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])

Benchmark Results

  • Coming Soon!

Notice

Hebrew-Gemma-11B-V2 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.