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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+Hebrew-Mistral-7B - bnb 4bits
+- Model creator: https://huggingface.co/yam-peleg/
+- Original model: https://huggingface.co/yam-peleg/Hebrew-Mistral-7B/
+
+
+
+
+Original model description:
+---
+license: apache-2.0
+language:
+- en
+- he
+library_name: transformers
+---
+# Hebrew-Mistral-7B
+
+Hebrew-Mistral-7B is an open-source Large Language Model (LLM) pretrained in hebrew and english pretrained with 7B billion parameters, based on Mistral-7B-v1.0 from Mistral.
+
+It has an extended hebrew tokenizer with 64,000 tokens and is continuesly pretrained from Mistral-7B on tokens in both English and Hebrew.
+
+The resulting model 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.
+
+### 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-Mistral-7B")
+model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B")
+
+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-Mistral-7B")
+model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B", 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-Mistral-7B")
+model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B", 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])
+```
+
+### Notice
+
+Hebrew-Mistral-7B 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.
+
+
+