RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-V2-gguf

Quantization made by Richard Erkhov.

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Hebrew-Gemma-11B-V2 - GGUF

• —Model creator: https://huggingface.co/yam-peleg/
• —Original model: https://huggingface.co/yam-peleg/Hebrew-Gemma-11B-V2/

Original model description: --- license: other licensename: gemma-terms-of-use licenselink: 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:

• —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.