license: apache-2.0
pipeline_tag: text-generation
language:
- en
- he
tags:
- instruction-tuned
base_model: dicta-il/dictalm2.0
inference: false
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 model contains the AWQ 4-bit quantized version of the instruct-tuned model designed for chat DictaLM-2.0-Instruct.
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. 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] 讗讬讝讛 专讜讟讘 讗讛讜讘 注诇讬讱? [/INST]
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]"
This format is available as a chat template via the apply_chat_template() method:
Example Code
Running this code requires under 5GB of GPU VRAM.
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("dicta-il/dictalm2.0-instruct-AWQ", device_map=device)
tokenizer = AutoTokenizer.from_pretrained("dicta-il/dictalm2.0-instruct-AWQ")
messages = [
{"role": "user", "content": "讗讬讝讛 专讜讟讘 讗讛讜讘 注诇讬讱?"},
{"role": "assistant", "content": "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!"},
{"role": "user", "content": "讛讗诐 讬砖 诇讱 诪转讻讜谞讬诐 诇诪讬讜谞讝?"}
]
encoded = tokenizer.apply_chat_template(messages, return_tensors="pt").to(device)
generated_ids = model.generate(encoded, max_new_tokens=50, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
# <s> [INST] 讗讬讝讛 专讜讟讘 讗讛讜讘 注诇讬讱? [/INST]
# 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]
# 讛谞讛 诪转讻讜谉 驻砖讜讟 讜拽诇 诇诪讬讜谞讝 讘讬转讬:
#
# 诪专讻讬讘讬诐:
# - 讘讬爪讛 讙讚讜诇讛 讗讞转
# - 2 讻驻讜转 讞讜诪抓 讬讬谉 诇讘谉
# - 1 讻祝
# (it stopped early because we set max_new_tokens=50)
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},
}