Create README.md

README.md

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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")
+model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Gemma-11B")
+
+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
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Gemma-11B")
+model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Gemma-11B", 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
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Gemma-11B")
+model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Gemma-11B", 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 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.