---
license: apache-2.0
pipeline_tag: text-generation
language:
  - en
  - he
tags:
- pretrained
inference:
  parameters:
    temperature: 0.7
---

[<img src="dicta-logo.jpg" width="300px"/>](https://dicta.org.il)


# Model Card for DictaLM-2.0-AWQ

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://example.com).

This model contains the AWQ 4-bit quantized version of the base model [DictaLM-2.0](https://huggingface.co/dicta-il/dictalm2.0).

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

Running this code requires less than 5GB of GPU VRAM.

```python
from transformers import pipeline

# This loads the model onto the GPU in bfloat16 precision
model = pipeline('text-generation', 'dicta-il/dictalm2.0-AWQ', 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=4, 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'}]
```

## 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
[Will be added soon]
```