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--- |
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license: other |
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license_name: gemma-terms-of-use |
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license_link: https://ai.google.dev/gemma/terms |
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language: |
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- en |
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- he |
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library_name: transformers |
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--- |
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# Hebrew-Gemma-11B-Instruct |
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- **Base Model:** [Hebrew-Gemma-11B](yam-peleg/Hebrew-Gemma-11B) |
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- **Instruct Model:** [Hebrew-Gemma-11B-Instruct](yam-peleg/Hebrew-Gemma-11B-Instruct) |
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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. |
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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. |
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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. |
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### Terms of Use |
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As an extention of Gemma-7B, this model is subject to the original license and terms of use by Google. |
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**Gemma-7B original Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent) |
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### Usage |
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Below are some code snippets on how to get quickly started with running the model. |
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First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. |
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### Running on CPU |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Gemma-11B-Instruct") |
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model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Gemma-11B-Instruct") |
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input_text = "ืฉืืื! ืื ืฉืืืื ืืืื?" |
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input_ids = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**input_ids) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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### Running on GPU |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Gemma-11B-Instruct") |
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model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Gemma-11B-Instruct", device_map="auto") |
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input_text = "ืฉืืื! ืื ืฉืืืื ืืืื?" |
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input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") |
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outputs = model.generate(**input_ids) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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### Running with 4-Bit precision |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Gemma-11B-Instruct") |
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model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Gemma-11B-Instruct", quantization_config = BitsAndBytesConfig(load_in_4bit=True)) |
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input_text = "ืฉืืื! ืื ืฉืืืื ืืืื?" |
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input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") |
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outputs = model.generate(**input_ids) |
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print(tokenizer.decode(outputs[0]) |
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``` |
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### Benchmark Results |
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- Coming Soon! |
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### Notice |
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Hebrew-Gemma-11B is a pretrained base model and therefore does not have any moderation mechanisms. |
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### Author |
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Trained by Yam Peleg. |
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