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--- |
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license: apache-2.0 |
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pipeline_tag: text-generation |
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language: |
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- en |
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- he |
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tags: |
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- instruction-tuned |
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base_model: dicta-il/dictalm2.0 |
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inference: false |
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--- |
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[<img src="https://i.ibb.co/5Lbwyr1/dicta-logo.jpg" width="300px"/>](https://dicta.org.il) |
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# Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities |
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The DictaLM-2.0-Instruct Large Language Model (LLM) is an instruct fine-tuned version of the [DictaLM-2.0](https://huggingface.co/dicta-il/dictalm2.0) generative model using a variety of conversation datasets. |
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For full details of this model please read our [release blog post](https://dicta.org.il/dicta-lm) or the [technical report](https://arxiv.org/abs/2407.07080). |
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This is the instruct-tuned model designed for chat in the GGUF format for use with [LM Studio](https://lmstudio.ai/) or [llama.cpp](https://github.com/ggerganov/llama.cpp). You can try the model out on a live demo [here](https://huggingface.co/spaces/dicta-il/dictalm2.0-instruct-demo). |
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There are two versions available - float16 precision (`*.F16.gguf`) and 4-bit quantized precision (`*.Q4_K_M.gguf`). |
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You can view and access the full collection of base/instruct unquantized/quantized versions of `DictaLM-2.0` [here](https://huggingface.co/collections/dicta-il/dicta-lm-20-collection-661bbda397df671e4a430c27). |
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## Instruction format |
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In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens followed by a line break. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. |
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E.g. |
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``` |
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text = """<s>[INST] 讗讬讝讛 专讜讟讘 讗讛讜讘 注诇讬讱? [/INST] |
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讟讜讘, 讗谞讬 讚讬 诪讞讘讘 讻诪讛 讟讬驻讜转 诪讬抓 诇讬诪讜谉 住讞讜讟 讟专讬. 讝讛 诪讜住讬祝 讘讚讬讜拽 讗转 讛讻诪讜转 讛谞讻讜谞讛 砖诇 讟注诐 讞诪爪诪抓 诇讻诇 诪讛 砖讗谞讬 诪讘砖诇 讘诪讟讘讞!</s>[INST] 讛讗诐 讬砖 诇讱 诪转讻讜谞讬诐 诇诪讬讜谞讝? [/INST]" |
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``` |
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This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: |
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## Using with LM Studio |
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When using with LM Studio, just search the hub for "dictalm2.0-instruct-GGUF", and the model in both precisions should appear. |
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Make sure to set the chat template correctly - initialize from the `mistral-instruct` template, and add a `\n` in the suffix box, like here: |
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<img src="https://i.ibb.co/D9MVgK2/lmstudio-dlm-template.png" width="400px" /> |
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In addition, the model doesn't support any system prompt, so make sure to remove the system prompt as well. |
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## Model Architecture |
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DictaLM-2.0-Instruct follows the [Zephyr-7B-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) recipe for fine-tuning an instruct model, with an extended instruct dataset for Hebrew. |
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## Limitations |
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The DictaLM 2.0 Instruct model is a demonstration that the base model can be fine-tuned to achieve compelling performance. |
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It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to |
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make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. |
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## Citation |
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If you use this model, please cite: |
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```bibtex |
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@misc{shmidman2024adaptingllmshebrewunveiling, |
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title={Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities}, |
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author={Shaltiel Shmidman and Avi Shmidman and Amir DN Cohen and Moshe Koppel}, |
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year={2024}, |
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eprint={2407.07080}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2407.07080}, |
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} |
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``` |
