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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](https://huggingface.co/yam-peleg/Hebrew-Gemma-11B) |
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- **Instruct Model:** [Hebrew-Gemma-11B-Instruct](https://huggingface.co/yam-peleg/Hebrew-Gemma-11B-Instruct) |
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The Hebrew-Gemma-11B-Instruct Large Language Model (LLM) is a instruct fine-tuned version of the [Hebrew-Gemma-11B](https://huggingface.co/yam-peleg/Hebrew-Gemma-11B) generative text model using a variety of conversation datasets. |
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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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# Instruction format |
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This format must be strictly respected, otherwise the model will generate sub-optimal outputs. |
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``` |
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<bos><start_of_turn>user |
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Write a hello world program<end_of_turn> |
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<start_of_turn>model |
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Here is a simple hellow world program<end_of_turn><eos> |
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``` |
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- The conversation starts with **`<bos>`**. |
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- Each turn is preceded by a **`<start_of_turn>`** delimiter and then the role of the entity (`user` or `model`). |
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- Turns finish with the **`<end_of_turn>`** token. |
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- Conversation finish with the **`<eos>`** token. |
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You can follow this format to build the prompt manually, if you need to do it without the tokenizer's chat template. |
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A simple example using the tokenizer's chat template: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_id = "Hebrew-Gemma-11B-Instruct" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda") |
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chat = [ |
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{ "role": "user", "content": "讻转讜讘 拽讜讚 驻砖讜讟 讘驻讬讬转讜谉 砖诪讚驻讬住 诇诪住讱 讗转 讛转讗专讬讱 砖诇 讛讬讜诐" }, |
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] |
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prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) |
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``` |
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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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### 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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