---
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-Instruct

- **Base Model:** [Hebrew-Gemma-11B](https://huggingface.co/yam-peleg/Hebrew-Gemma-11B)
- **Instruct Model:** [Hebrew-Gemma-11B-Instruct](https://huggingface.co/yam-peleg/Hebrew-Gemma-11B-Instruct)

The Hebrew-Gemma-11B-Instruct Large Language Model (LLM) is a instruct fine-tuned version of the [Hebrew-Gemma-11B](https://huggingface.co/yam-peleg/Hebrew-Gemma-11B) generative text model using a variety of conversation datasets.

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.


# Instruction format

This format must be strictly respected, otherwise the model will generate sub-optimal outputs.

```
<bos><start_of_turn>user
Write a hello world program<end_of_turn>
<start_of_turn>model
Here is a simple hellow world program<end_of_turn>
<eos>
```

Each turn is preceded by a <start_of_turn> delimiter and then the role of the entity (either user, for content supplied by the user, or model for LLM responses). Turns finish with the <end_of_turn> token.
You can follow this format to build the prompt manually, if you need to do it without the tokenizer's chat template.

A simple example:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Hebrew-Gemma-11B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda")

chat = [
    { "role": "user", "content": "כתוב קוד פשוט בפייתון שמדפיס למסך את התאריך של היום" },
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
```

### Terms of Use

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

### Benchmark Results

- Coming Soon!


### Notice

Hebrew-Gemma-11B is a pretrained base model and therefore does not have any moderation mechanisms.


### Author

Trained by Yam Peleg.