Downloads · 30 days
26
3% of all-time downloads
dicta-il/DictaLM-3.0-1.7B-Instruct-W4A16
DictaLM-3.0-1.7B-Instruct-W4A16 is a text generation model from dicta-il. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
<img src="https://i.ibb.co/5Lbwyr1/dicta-logo.jpg" width="300px"/
Downloads · 30 days
26
3% of all-time downloads
All-time downloads
1K
Public
Parameters
2B
2 GB on disk
Likes
0
Public
Click a slice to open those files.
.safetensors2 GB · 99%
How the weights are stored.
I321.4B · 69%
From the Hugging Face model README
<img src="https://i.ibb.co/5Lbwyr1/dicta-logo.jpg" width="300px"/>
Dicta-LM 3.0 is a powerful open-weight collection of LLMs, trained on extensive corpora of Hebrew and English texts. The models are available for download and for unlimited use. The models set a new SOTA for their weight-class for Hebrew, both as base models and chat models.
This is the 1.7-billion-parameter instruct model, originally initialized from Qwen3-1.7B-Base.
This version of the model is quantized to 4-bits (with 16-bit activations), allowing for inference with significantly less memory although with weaker performance.
For full details of this model please read our release blog post or the technical report.
You can view and access the full collection of base/instruct unquantized/quantized versions of DictaLM 3.0 here.
In order to leverage instruction fine-tuning, your prompt should be rendered using the chat template specified for this model. Most libraries deal with this automatically, so you can just let them do it.
vllm serve dicta-il/DictaLM-3.0-1.7B-Instruct-W4A16 --enable-auto-tool-choice --tool-call-parser hermes
And then you can access it via the openai library:
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:8000/v1",
api_key="sk-no-key-required"
)
response = client.chat.completions.create(
model="dicta-il/DictaLM-3.0-1.7B-Instruct-W4A16",
messages=[
{"role": "user", "content": "Hello, how are you?"}
],
)
print(response.choices[0].message.content)
The model supports tool-calling, enabling integration with external tools and APIs. For example how to use the tool calling, see the vLLM documentation.
If you use this model, please cite:
@article{Shmidman2025DictaLM3,
title={{Dicta-LM 3.0: Advancing The Frontier of Hebrew Sovereign LLMs}},
author={Shaltiel Shmidman and Avi Shmidman and Amir DN Cohen and Moshe Koppel},
year={2025},
publisher={{DICTA / Jerusalem, Israel}},
note={https://www.dicta.org.il/publications/DictaLM_3_0___Techincal_Report.pdf}
}