179 lines
12 KiB
Markdown
179 lines
12 KiB
Markdown
---
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base_model: ilsp/Meltemi-7B-Instruct-v1
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license: apache-2.0
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model_name: Meltemi-7B-Instruct-v1
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pipeline_tag: text-generation
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quantized_by: SPAHE
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tags:
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- finetuned
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---
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<!-- markdownlint-disable MD041 -->
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# Meltemi 7B Instruct v1 - GGUF
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- Original model: [Meltemi 7B Instruct v1](https://huggingface.co/ilsp/Meltemi-7B-Instruct-v1)
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<!-- description start -->
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## Description
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This repository contains GGUF format model files for [ilsp's Meltemi 7B Instruct v1](https://huggingface.co/ilsp/Meltemi-7B-Instruct-v1), optimized for different performance and storage requirements. Each model variant has been carefully quantized or preserved in floating-point format to suit varying demands for quality, speed, and memory usage.
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<!-- description end -->
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<!-- README_GGUF.md-provided-files start -->
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## Provided files
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| Name | Quantization Method | Precision (Bits) | File Size | Max RAM Required | Use Case |
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| ------------------------------------------------------------------------------------------------------------------------------------------- | ------------------- | ---------------- | --------- | ---------------- | ------------------------------------------- |
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| [meltemi-7b-instruct-v1_q3_k_s.gguf](https://huggingface.co/SPAHE/Meltemi-7B-Instruct-v1-GGUF/blob/main/meltemi-7b-instruct-v1_q3_k_s.gguf) | Q3_K_S | 3 | 3.32 GB | 3.69 GB | Medium, high quality loss |
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| [meltemi-7b-instruct-v1_q4_k_m.gguf](https://huggingface.co/SPAHE/Meltemi-7B-Instruct-v1-GGUF/blob/main/meltemi-7b-instruct-v1_q4_k_m.gguf) | Q4_K_M | 4 | 4.54 GB | 4.41 GB | Medium, balanced quality |
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| [meltemi-7b-instruct-v1_q6_k.gguf](https://huggingface.co/SPAHE/Meltemi-7B-Instruct-v1-GGUF/blob/main/meltemi-7b-instruct-v1_q6_k.gguf) | Q6_K | 6 | 6.14 GB | 5.92 GB | Medium, low quality loss |
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| [meltemi-7b-instruct-v1_q8_0.gguf](https://huggingface.co/SPAHE/Meltemi-7B-Instruct-v1-GGUF/blob/main/meltemi-7b-instruct-v1_q8_0.gguf) | Q8_0 | 8 | 7.95 GB | 7.30 GB | Large, low quality loss |
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| [meltemi-7b-instruct-v1_f16.gguf](https://huggingface.co/SPAHE/Meltemi-7B-Instruct-v1-GGUF/blob/main/meltemi-7b-instruct-v1_f16.gguf) | F16 | 16 | 15.00 GB | 14.20 GB | Very large, extremely low quality loss |
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| [meltemi-7b-instruct-v1_f32.gguf](https://huggingface.co/SPAHE/Meltemi-7B-Instruct-v1-GGUF/blob/main/meltemi-7b-instruct-v1_f32.gguf) | F32 | 32 | 27.90 GB | 29.30 GB | Very very large, extremely low quality loss |
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**Note**: The above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
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<!-- README_GGUF.md-provided-files end -->
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<!-- README_GGUF.md-how-to-download start -->
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## How to Download GGUF Files
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### For Manual Downloaders
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It is recommended not to clone the entire repository due to the large file sizes and multiple quantization formats available. Most users will benefit from selecting and downloading a single, specific model file that best suits their requirements.
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### Automated Download via Client Libraries
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For convenience, the following clients and libraries can automate the download process and offer a selection of available models:
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- **LM Studio**: Provides an integrated environment for downloading and utilizing models directly.
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### Downloading with Command Line
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The `huggingface-hub` Python library simplifies the process of downloading specific model files. Install the library with:
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```shell
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pip install huggingface-hub
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```
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To download a model file directly to your current directory, execute:
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```shell
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huggingface-cli download SPAHE/Meltemi-7B-Instruct-v1-GGUF --filename meltemi-7b-instruct-v1_q8_0.gguf --output-dir .
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```
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This command ensures a high-speed download of the specific GGUF file you need without unnecessary data.
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<!-- README_GGUF.md-how-to-download end -->
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<!-- original-model-card start -->
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# Original model card: ilsp's Meltemi 7B Instruct v1
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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, return_tensors="pt")
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input_prompt = prompt.to(device)
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outputs = model.generate(input_prompt, 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, return_tensors="pt")
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input_prompt = prompt.to(device)
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outputs = model.generate(input_prompt, 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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# 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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<!-- original-model-card end -->
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