license, license_name, license_link, language, library_name
| license | license_name | license_link | language | library_name | ||
|---|---|---|---|---|---|---|
| other | gemma-terms-of-use | https://ai.google.dev/gemma/terms |
|
transformers |
Hebrew-Gemma-11B-V2
An updated version of Hebrew-Gemma-11B that was trained longer and had some bugs fixes.
Base Models:
- 07.03.2024: Hebrew-Gemma-11B
- 16.03.2024: Hebrew-Gemma-11B-V2
Instruct Models:
- 07.03.2024: Hebrew-Gemma-11B-Instruct
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 = "שלום! מה שלומך היום?"
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 = "שלום! מה שלומך היום?"
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 = "שלום! מה שלומך היום?"
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.