Hebrew-Gemma-11B-Instruct
Base Models:
- 07.03.2024: Hebrew-Gemma-11B
- 16.03.2024: Hebrew-Gemma-11B-V2
Instruct Models:
- 07.03.2024: Hebrew-Gemma-11B-Instruct
The Hebrew-Gemma-11B-Instruct Large Language Model (LLM) is a instruct fine-tuned version of the 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>
- The conversation starts with
<bos>. - Each turn is preceded by a
<start_of_turn>delimiter and then the role of the entity (userormodel). - Turns finish with the
<end_of_turn>token. - Conversation finish with the
<eos>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 using the tokenizer's chat template:
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.
Authors
- Trained by Yam Peleg.
- In collaboration with Jonathan Rouach and Arjeo, inc.