Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities
The DictaLM-2.0-Instruct Large Language Model (LLM) is an instruct fine-tuned version of the DictaLM-2.0 generative model using a variety of conversation datasets.
For full details of this model please read our release blog post or the technical report.
This is the instruct-tuned model designed for chat in the GGUF format for use with LM Studio or llama.cpp. You can try the model out on a live demo here.
There are two versions available - float16 precision (*.F16.gguf) and 4-bit quantized precision (*.Q4_K_M.gguf).
You can view and access the full collection of base/instruct unquantized/quantized versions of DictaLM-2.0 here.
Instruction format
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.
E.g.
text = """<s>[INST] איזה רוטב אהוב עליך? [/INST]
טוב, אני די מחבב כמה טיפות מיץ לימון סחוט טרי. זה מוסיף בדיוק את הכמות הנכונה של טעם חמצמץ לכל מה שאני מבשל במטבח!</s>[INST] האם יש לך מתכונים למיונז? [/INST]"
This format is available as a chat template via the apply_chat_template() method:
Using with LM Studio
When using with LM Studio, just search the hub for "dictalm2.0-instruct-GGUF", and the model in both precisions should appear.
Make sure to set the chat template correctly - initialize from the mistral-instruct template, and add a \n in the suffix box, like here:
In addition, the model doesn't support any system prompt, so make sure to remove the system prompt as well.
Model Architecture
DictaLM-2.0-Instruct follows the Zephyr-7B-beta recipe for fine-tuning an instruct model, with an extended instruct dataset for Hebrew.
Limitations
The DictaLM 2.0 Instruct model is a demonstration that the base model can be fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
Citation
If you use this model, please cite:
@misc{shmidman2024adaptingllmshebrewunveiling,
title={Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities},
author={Shaltiel Shmidman and Avi Shmidman and Amir DN Cohen and Moshe Koppel},
year={2024},
eprint={2407.07080},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2407.07080},
}