96 lines
4.4 KiB
Markdown
96 lines
4.4 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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- instruction-tuned
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base_model: dicta-il/dictalm2.0
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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-Instruct Large Language Model (LLM) is an instruct fine-tuned version of the [DictaLM-2.0](https://huggingface.co/dicta-il/dictalm2.0) generative model using a variety of conversation datasets.
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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 instruct-tuned full-precision model designed for chat. You can try the model out on a live demo [here](https://huggingface.co/spaces/dicta-il/dictalm2.0-instruct-demo).
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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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## Instruction format
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In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
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E.g.
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```
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text = """<s>[INST] איזה רוטב אהוב עליך? [/INST]
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טוב, אני די מחבב כמה טיפות מיץ לימון סחוט טרי. זה מוסיף בדיוק את הכמות הנכונה של טעם חמצמץ לכל מה שאני מבשל במטבח!</s>[INST] האם יש לך מתכונים למיונז? [/INST]"
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```
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This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
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## Example Code
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained("dicta-il/dictalm2.0-instruct", torch_dtype=torch.bfloat16, device_map=device)
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tokenizer = AutoTokenizer.from_pretrained("dicta-il/dictalm2.0-instruct")
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messages = [
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{"role": "user", "content": "איזה רוטב אהוב עליך?"},
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{"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!"},
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{"role": "user", "content": "האם יש לך מתכונים למיונז?"}
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]
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encoded = tokenizer.apply_chat_template(messages, return_tensors="pt").to(device)
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generated_ids = model.generate(encoded, max_new_tokens=50, do_sample=True)
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decoded = tokenizer.batch_decode(generated_ids)
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print(decoded[0])
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# <s> [INST] איזה רוטב אהוב עליך? [/INST]
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# טוב, אני די מחבב כמה טיפות מיץ לימון סחוט טרי. זה מוסיף בדיוק את הכמות הנכונה של טעם חמצמץ לכל מה שאני מבשל במטבח!</s> [INST] האם יש לך מתכונים למיונז? [/INST]
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# בטח, הנה מתכון בסיסי וקל להכנת מיונז ביתי!
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#
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# מרכיבים:
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# - 2 חלמונים גדולים
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# - 1 כף חומץ יין לבן
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# (it stopped early because we set max_new_tokens=50)
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```
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## Model Architecture
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DictaLM-2.0-Instruct follows the [Zephyr-7B-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) recipe for fine-tuning an instruct model, with an extended instruct dataset for Hebrew.
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## Limitations
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The DictaLM 2.0 Instruct model is a demonstration that the base model can be fine-tuned to achieve compelling performance.
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It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
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make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
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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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