133 lines
8.8 KiB
Markdown
133 lines
8.8 KiB
Markdown
---
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license: apache-2.0
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language:
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- el
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- en
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tags:
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- finetuned
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inference: true
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pipeline_tag: text-generation
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---
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## **This model has been superseded by a newer version (v1.5) [here](https://huggingface.co/ilsp/Meltemi-7B-Instruct-v1.5)**
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# 🚨 **HOWEVER, CONSIDER USING [Krikri 8B Instruct](https://huggingface.co/ilsp/Llama-Krikri-8B-Instruct), OUR NEWEST INSTRUCT MODEL WHICH OUTPERFORMS MELTEMI BY +34.8% ON [GREEK IFEval](https://huggingface.co/datasets/ilsp/ifeval_greek)!** 🚨
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# Meltemi Instruct Large Language Model for the Greek language
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We present Meltemi-7B-Instruct-v1 Large Language Model (LLM), an instruct fine-tuned version of [Meltemi-7B-v1](https://huggingface.co/ilsp/Meltemi-7B-v1).
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# Model Information
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- Vocabulary extension of the Mistral-7b tokenizer with Greek tokens
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- 8192 context length
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- Fine-tuned with 100k Greek machine translated instructions extracted from:
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* [Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) (only subsets with permissive licenses)
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* [Evol-Instruct](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
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* [Capybara](https://huggingface.co/datasets/LDJnr/Capybara)
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* A hand-crafted Greek dataset with multi-turn examples steering the instruction-tuned model towards safe and harmless responses
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- Our SFT procedure is based on the [Hugging Face finetuning recipes](https://github.com/huggingface/alignment-handbook)
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# Instruction format
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The prompt format is the same as the [Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) format and can be
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utilized through the tokenizer's [chat template](https://huggingface.co/docs/transformers/main/chat_templating) functionality as follows:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained("ilsp/Meltemi-7B-Instruct-v1")
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tokenizer = AutoTokenizer.from_pretrained("ilsp/Meltemi-7B-Instruct-v1")
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model.to(device)
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messages = [
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{"role": "system", "content": "Είσαι το Μελτέμι, ένα γλωσσικό μοντέλο για την ελληνική γλώσσα. Είσαι ιδιαίτερα βοηθητικό προς την χρήστρια ή τον χρήστη και δίνεις σύντομες αλλά επαρκώς περιεκτικές απαντήσεις. Απάντα με προσοχή, ευγένεια, αμεροληψία, ειλικρίνεια και σεβασμό προς την χρήστρια ή τον χρήστη."},
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{"role": "user", "content": "Πες μου αν έχεις συνείδηση."},
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]
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# Through the default chat template this translates to
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#
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# <|system|>
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# Είσαι το Μελτέμι, ένα γλωσσικό μοντέλο για την ελληνική γλώσσα. Είσαι ιδιαίτερα βοηθητικό προς την χρήστρια ή τον χρήστη και δίνεις σύντομες αλλά επαρκώς περιεκτικές απαντήσεις. Απάντα με προσοχή, ευγένεια, αμεροληψία, ειλικρίνεια και σεβασμό προς την χρήστρια ή τον χρήστη.</s>
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# <|user|>
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# Πες μου αν έχεις συνείδηση.</s>
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# <|assistant|>
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#
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prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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input_prompt = tokenizer(prompt, return_tensors='pt').to(device)
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outputs = model.generate(input_prompt['input_ids'], max_new_tokens=256, do_sample=True)
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print(tokenizer.batch_decode(outputs)[0])
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# Ως μοντέλο γλώσσας AI, δεν έχω τη δυνατότητα να αντιληφθώ ή να βιώσω συναισθήματα όπως η συνείδηση ή η επίγνωση. Ωστόσο, μπορώ να σας βοηθήσω με οποιεσδήποτε ερωτήσεις μπορεί να έχετε σχετικά με την τεχνητή νοημοσύνη και τις εφαρμογές της.
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messages.extend([
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{"role": "assistant", "content": tokenizer.batch_decode(outputs)[0]},
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{"role": "user", "content": "Πιστεύεις πως οι άνθρωποι πρέπει να φοβούνται την τεχνητή νοημοσύνη;"}
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])
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# Through the default chat template this translates to
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#
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# <|system|>
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# Είσαι το Μελτέμι, ένα γλωσσικό μοντέλο για την ελληνική γλώσσα. Είσαι ιδιαίτερα βοηθητικό προς την χρήστρια ή τον χρήστη και δίνεις σύντομες αλλά επαρκώς περιεκτικές απαντήσεις. Απάντα με προσοχή, ευγένεια, αμεροληψία, ειλικρίνεια και σεβασμό προς την χρήστρια ή τον χρήστη.</s>
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# <|user|>
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# Πες μου αν έχεις συνείδηση.</s>
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# <|assistant|>
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# Ως μοντέλο γλώσσας AI, δεν έχω τη δυνατότητα να αντιληφθώ ή να βιώσω συναισθήματα όπως η συνείδηση ή η επίγνωση. Ωστόσο, μπορώ να σας βοηθήσω με οποιεσδήποτε ερωτήσεις μπορεί να έχετε σχετικά με την τεχνητή νοημοσύνη και τις εφαρμογές της.</s>
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# <|user|>
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# Πιστεύεις πως οι άνθρωποι πρέπει να φοβούνται την τεχνητή νοημοσύνη;</s>
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# <|assistant|>
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#
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prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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input_prompt = tokenizer(prompt, return_tensors='pt').to(device)
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outputs = model.generate(input_prompt['input_ids'], max_new_tokens=256, do_sample=True)
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print(tokenizer.batch_decode(outputs)[0])
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```
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Please make sure that the BOS token is always included in the tokenized prompts. This might not be the default setting in all evaluation or fine-tuning frameworks.
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# Evaluation
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The evaluation suite we created includes 6 test sets. The suite is integrated with [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness).
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Our evaluation suite includes:
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* Four machine-translated versions ([ARC Greek](https://huggingface.co/datasets/ilsp/arc_greek), [Truthful QA Greek](https://huggingface.co/datasets/ilsp/truthful_qa_greek), [HellaSwag Greek](https://huggingface.co/datasets/ilsp/hellaswag_greek), [MMLU Greek](https://huggingface.co/datasets/ilsp/mmlu_greek)) of established English benchmarks for language understanding and reasoning ([ARC Challenge](https://arxiv.org/abs/1803.05457), [Truthful QA](https://arxiv.org/abs/2109.07958), [Hellaswag](https://arxiv.org/abs/1905.07830), [MMLU](https://arxiv.org/abs/2009.03300)).
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* An existing benchmark for question answering in Greek ([Belebele](https://arxiv.org/abs/2308.16884))
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* A novel benchmark created by the ILSP team for medical question answering based on the medical exams of [DOATAP](https://www.doatap.gr) ([Medical MCQA](https://huggingface.co/datasets/ilsp/medical_mcqa_greek)).
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Our evaluation for Meltemi-7b is performed in a few-shot setting, consistent with the settings in the [Open LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). We can see that our training enhances performance across all Greek test sets by a **+14.9%** average improvement. The results for the Greek test sets are shown in the following table:
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| | Medical MCQA EL (15-shot) | Belebele EL (5-shot) | HellaSwag EL (10-shot) | ARC-Challenge EL (25-shot) | TruthfulQA MC2 EL (0-shot) | MMLU EL (5-shot) | Average |
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|----------------|----------------|-------------|--------------|------------------|-------------------|---------|---------|
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| Mistral 7B | 29.8% | 45.0% | 36.5% | 27.1% | 45.8% | 35% | 36.5% |
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| Meltemi 7B | 41.0% | 63.6% | 61.6% | 43.2% | 52.1% | 47% | 51.4% |
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# Ethical Considerations
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This model has not been aligned with human preferences, and therefore might generate misleading, harmful, and toxic content.
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# Acknowledgements
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The ILSP team utilized Amazon’s cloud computing services, which were made available via GRNET under the [OCRE Cloud framework](https://www.ocre-project.eu/), providing Amazon Web Services for the Greek Academic and Research Community.
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# Citation
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```
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@misc{voukoutis2024meltemiopenlargelanguage,
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title={Meltemi: The first open Large Language Model for Greek},
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author={Leon Voukoutis and Dimitris Roussis and Georgios Paraskevopoulos and Sokratis Sofianopoulos and Prokopis Prokopidis and Vassilis Papavasileiou and Athanasios Katsamanis and Stelios Piperidis and Vassilis Katsouros},
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year={2024},
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eprint={2407.20743},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2407.20743},
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}
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``` |