Dicta-LM 2.0 Collection
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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.
This model contains the GPTQ 4-bit quantized version of the base model DictaLM-2.0.
You can view and access the full collection of base/instruct unquantized/quantized versions of DictaLM-2.0
here.
Running this code requires ~5.1GB of GPU VRAM.
from transformers import pipeline
# This loads the model onto the GPU in bfloat16 precision
model = pipeline('text-generation', 'dicta-il/dictalm2.0-GPTQ', 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'}]
DictaLM-2.0 is based on the Mistral-7B-v0.1 model with the following changes:
DictaLM 2.0 is a pretrained base model and therefore does not have any moderation mechanisms.
If you use this model, please cite:
[Will be added soon]