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Model: dicta-il/dictalm2.0 Source: Original Platform
2026-05-10 11:31:27 +08:00
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+---
+license: apache-2.0
+pipeline_tag: text-generation
+language:
+  - en
+  - he
+tags:
+- pretrained
+inference:
+  parameters:
+    temperature: 0.7
+---
+
+[<img src="https://i.ibb.co/5Lbwyr1/dicta-logo.jpg" width="300px"/>](https://dicta.org.il)
+
+# Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities
+
+The DictaLM-2.0 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters trained to specialize in Hebrew text. 
+
+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).
+
+This is the full-precision base model. 
+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).
+
+## Example Code
+
+```python
+from transformers import pipeline
+import torch
+
+# This loads the model onto the GPU in bfloat16 precision
+model = pipeline('text-generation', 'dicta-il/dictalm2.0', torch_dtype=torch.bfloat16, device_map='cuda')
+
+# Sample few shot examples
+prompt = """
+עבר: הלכתי
+עתיד: אלך
+
+עבר: שמרתי
+עתיד: אשמור
+
+עבר: שמעתי
+עתיד: אשמע
+
+עבר: הבנתי
+עתיד:
+"""
+
+print(model(prompt.strip(), do_sample=False, max_new_tokens=8, stop_sequence='\n'))
+# [{'generated_text': 'עבר: הלכתי\nעתיד: אלך\n\nעבר: שמרתי\nעתיד: אשמור\n\nעבר: שמעתי\nעתיד: אשמע\n\nעבר: הבנתי\nעתיד: אבין\n\n'}]
+```
+
+## Example Code - 4-Bit
+
+There are already pre-quantized 4-bit models using the `GPTQ` and `AWQ` methods available for use: [DictaLM-2.0-AWQ](https://huggingface.co/dicta-il/dictalm2.0-AWQ) and [DictaLM-2.0-GPTQ](https://huggingface.co/dicta-il/dictalm2.0-GPTQ).
+
+For dynamic quantization on the go, here is sample code which loads the model onto the GPU using the `bitsandbytes` package, requiring :
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model = AutoModelForCausalLM.from_pretrained('dicta-il/dictalm2.0', torch_dtype=torch.bfloat16, device_map='cuda', load_in_4bit=True)
+tokenizer = AutoTokenizer.from_pretrained('dicta-il/dictalm2.0')
+
+prompt = """
+עבר: הלכתי
+עתיד: אלך
+
+עבר: שמרתי
+עתיד: אשמור
+
+עבר: שמעתי
+עתיד: אשמע
+
+עבר: הבנתי
+עתיד:
+"""
+
+encoded = tokenizer(prompt.strip(), return_tensors='pt').to(model.device)
+print(tokenizer.batch_decode(model.generate(**encoded, do_sample=False, max_new_tokens=4)))
+# ['<s> עבר: הלכתי\nעתיד: אלך\n\nעבר: שמרתי\nעתיד: אשמור\n\nעבר: שמעתי\nעתיד: אשמע\n\nעבר: הבנתי\nעתיד: אבין\n\n']
+```
+
+
+## Model Architecture
+
+DictaLM-2.0 is based on the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) model with the following changes:
+- An extended tokenizer with 1,000 injected tokens specifically for Hebrew, increasing the compression rate from 5.78 tokens/word to 2.76 tokens/word.  
+- Continued pretraining on over 190B tokens of naturally occuring text, 50% Hebrew and 50% English.
+
+## Notice
+
+DictaLM 2.0 is a pretrained base model and therefore does not have any moderation mechanisms.
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@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}, 
+}
+```