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---
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
> **TL;DR:** Continued pretraining of the previous hebrew base model & bug fixes.
### Base Models:
- **07.03.2024:** [Hebrew-Gemma-11B](https://huggingface.co/yam-peleg/Hebrew-Gemma-11B)
- **16.03.2024:** [Hebrew-Gemma-11B-V2](https://huggingface.co/yam-peleg/Hebrew-Gemma-11B-V2)
### Instruct Models:
- **07.03.2024:** [Hebrew-Gemma-11B-Instruct](https://huggingface.co/yam-peleg/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](https://www.kaggle.com/models/google/gemma/license/consent)
### 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
```python
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 = "ืฉืืื! ืื ืฉืืืื ืืืื?"
input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
### Running on GPU
```python
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 = "ื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
```python
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 = "ืฉืืื! ืื ืฉืืืื ืืืื?"
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. |