110 lines
3.7 KiB
Markdown
110 lines
3.7 KiB
Markdown
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---
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license: apache-2.0
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pipeline_tag: text-generation
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language:
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- en
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- he
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tags:
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- pretrained
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inference:
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parameters:
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temperature: 0.7
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---
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[<img src="https://i.ibb.co/5Lbwyr1/dicta-logo.jpg" width="300px"/>](https://dicta.org.il)
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# Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities
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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.
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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).
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This is the full-precision base model.
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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).
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## Example Code
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```python
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from transformers import pipeline
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import torch
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# This loads the model onto the GPU in bfloat16 precision
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model = pipeline('text-generation', 'dicta-il/dictalm2.0', torch_dtype=torch.bfloat16, device_map='cuda')
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# Sample few shot examples
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prompt = """
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עבר: הלכתי
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עתיד: אלך
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עבר: שמרתי
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עתיד: אשמור
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עבר: שמעתי
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עתיד: אשמע
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עבר: הבנתי
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עתיד:
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"""
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print(model(prompt.strip(), do_sample=False, max_new_tokens=8, stop_sequence='\n'))
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# [{'generated_text': 'עבר: הלכתי\nעתיד: אלך\n\nעבר: שמרתי\nעתיד: אשמור\n\nעבר: שמעתי\nעתיד: אשמע\n\nעבר: הבנתי\nעתיד: אבין\n\n'}]
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```
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## Example Code - 4-Bit
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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).
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For dynamic quantization on the go, here is sample code which loads the model onto the GPU using the `bitsandbytes` package, requiring :
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model = AutoModelForCausalLM.from_pretrained('dicta-il/dictalm2.0', torch_dtype=torch.bfloat16, device_map='cuda', load_in_4bit=True)
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tokenizer = AutoTokenizer.from_pretrained('dicta-il/dictalm2.0')
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prompt = """
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עבר: הלכתי
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עתיד: אלך
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עבר: שמרתי
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עתיד: אשמור
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עבר: שמעתי
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עתיד: אשמע
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עבר: הבנתי
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עתיד:
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"""
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encoded = tokenizer(prompt.strip(), return_tensors='pt').to(model.device)
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print(tokenizer.batch_decode(model.generate(**encoded, do_sample=False, max_new_tokens=4)))
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# ['<s> עבר: הלכתי\nעתיד: אלך\n\nעבר: שמרתי\nעתיד: אשמור\n\nעבר: שמעתי\nעתיד: אשמע\n\nעבר: הבנתי\nעתיד: אבין\n\n']
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```
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## Model Architecture
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DictaLM-2.0 is based on the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) model with the following changes:
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- 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.
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- Continued pretraining on over 190B tokens of naturally occuring text, 50% Hebrew and 50% English.
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## Notice
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DictaLM 2.0 is a pretrained base model and therefore does not have any moderation mechanisms.
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## Citation
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If you use this model, please cite:
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```bibtex
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@misc{shmidman2024adaptingllmshebrewunveiling,
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title={Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities},
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author={Shaltiel Shmidman and Avi Shmidman and Amir DN Cohen and Moshe Koppel},
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year={2024},
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eprint={2407.07080},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2407.07080},
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}
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```
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