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README.md

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+---
+license: apache-2.0
+pipeline_tag: text-generation
+language:
+  - en
+  - he
+tags:
+- pretrained
+inference:
+  parameters:
+    temperature: 0.6
+---
+
+[<img src="https://i.ibb.co/5Lbwyr1/dicta-logo.jpg" width="300px"/>](https://dicta.org.il)
+
+# Dicta-LM 3.0: Advancing The Frontier of Hebrew Sovereign LLMs 
+
+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 our flagship model, a 24-billion-parameter *reasoning* model, with full precision (BF16), originally initialized from [Mistral-Small-3.1-24B-Base-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503).
+
+This model is a reasoning chat model, which means that before responding to any given message from the user, the model first thinks out the right way to respond in a designated thinking block. 
+
+<br/>
+
+&#128640; Try it out here: [chat.dicta.org.il](https://chat.dicta.org.il)
+
+<br/>
+
+For full details of this model please read our [release blog post](https://dicta.org.il/dicta-lm-3) or the [technical report](https://www.dicta.org.il/publications/DictaLM_3_0___Techincal_Report.pdf).
+
+You can view and access the full collection of base/instruct unquantized/quantized versions of `DictaLM 3.0` [here](https://huggingface.co/collections/dicta-il/dictalm-30-collection).
+
+## Instruction format
+
+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. 
+
+## Usage
+
+We recommend using vLLM, but you can use Transformers as well:
+
+### Transformers
+
+```python
+
+from transformers import pipeline
+
+generator = pipeline('text-generation', model="dicta-il/DictaLM-3.0-24B-Thinking")
+
+messages = [
+    {"role": "user", "content": "איזה רוטב אהוב עליך?"},
+    {"role": "assistant", "content": "טוב, אני די מחבב כמה טיפות מיץ לימון סחוט טרי. זה מוסיף בדיוק את הכמות הנכונה של טעם חמצמץ לכל מה שאני מבשל במטבח!"},
+    {"role": "user", "content": "האם יש לך מתכונים למיונז?"}
+]
+
+print(generator(messages)[0]['generated_text'][-1]) # just print the last message
+# {'role': 'assistant', 'content': 'המשתמש שואל: "האם יש לך מתכונים למיונז?" עלינו להגיב עם מתכון למיונז. ספקו מתכון קלאסי, אולי גם וריאציות (מיונז פליאו, מיונז טבעוני, מיונז חריף, וכו\'). ספקו הוראות שלב אחר שלב. צריך להיות בטון ידידותי.</think>בטח! מיונז ביתי הוא באמת פשוט להכנה ומעניק לכם שליטה מלאה על המרכיבים, הטעם והמרקם. להלן המתכון הקלאסי, בתוספת כמה וריאציות מהנות שתוכלו להתנסות בהן.\n\n---\n\n## 1️⃣ מיונז קלאסי (מיונז בסיסי)\n\n| מרכיב | כמות |\n|------------|----------|\n| חלמון ביצה גדול (או 2 חלמונים בינוניים) | 1 |\n| חרדל דיז\'ון | 1 כפית |\n| מיץ לימון טרי (או ח'}
+```
+
+### vLLM
+
+```bash
+vllm serve dicta-il/DictaLM-3.0-24B-Thinking --enable-auto-tool-choice --tool-call-parser hermes --reasoning_parser deepseek_r1
+```
+
+And then you can access it via the openai library:
+
+```python
+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-3.0-24B-Thinking",
+    messages=[
+        {"role": "user", "content": "Hello, how are you?"}
+    ],
+)
+
+print(response.choices[0].message.content)
+```
+
+> The reasoning traces should be available in the response structure in the designated fild. 
+
+The model supports tool-calling, enabling integration with external tools and APIs. For example how to use the tool calling, see the [vLLM documentation](https://docs.vllm.ai/en/stable/features/tool_calling/#tool-calling).
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@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}
+}
+```

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