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Model: SPAHE/Meltemi-7B-Instruct-v1-GGUF
Source: Original Platform

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README.md

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+---
+base_model: ilsp/Meltemi-7B-Instruct-v1
+license: apache-2.0
+model_name: Meltemi-7B-Instruct-v1
+pipeline_tag: text-generation
+quantized_by: SPAHE
+tags:
+  - finetuned
+---
+
+<!-- markdownlint-disable MD041 -->
+
+# Meltemi 7B Instruct v1 - GGUF
+
+- Original model: [Meltemi 7B Instruct v1](https://huggingface.co/ilsp/Meltemi-7B-Instruct-v1)
+
+<!-- description start -->
+
+## Description
+
+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.
+
+<!-- description end -->
+
+<!-- README_GGUF.md-provided-files start -->
+
+## Provided files
+| Name                                                                                                                                        | Quantization Method | Precision (Bits) | File Size | Max RAM Required | Use Case                                    |
+| ------------------------------------------------------------------------------------------------------------------------------------------- | ------------------- | ---------------- | --------- | ---------------- | ------------------------------------------- |
+| [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                   |
+| [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                    |
+| [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                    |
+| [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                     |
+| [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      |
+| [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 |
+
+**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.
+
+<!-- README_GGUF.md-provided-files end -->
+
+<!-- README_GGUF.md-how-to-download start -->
+
+## How to Download GGUF Files
+
+### For Manual Downloaders
+
+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.
+
+### Automated Download via Client Libraries
+
+For convenience, the following clients and libraries can automate the download process and offer a selection of available models:
+
+- **LM Studio**: Provides an integrated environment for downloading and utilizing models directly.
+
+### Downloading with Command Line
+
+The `huggingface-hub` Python library simplifies the process of downloading specific model files. Install the library with:
+
+```shell
+pip install huggingface-hub
+```
+
+To download a model file directly to your current directory, execute:
+
+```shell
+huggingface-cli download SPAHE/Meltemi-7B-Instruct-v1-GGUF --filename meltemi-7b-instruct-v1_q8_0.gguf --output-dir .
+```
+
+This command ensures a high-speed download of the specific GGUF file you need without unnecessary data.
+
+<!-- README_GGUF.md-how-to-download end -->
+
+<!-- original-model-card start -->
+
+# Original model card: ilsp's Meltemi 7B Instruct v1
+
+# Meltemi Instruct Large Language Model for the Greek language
+
+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).
+
+# Model Information
+
+- Vocabulary extension of the Mistral-7b tokenizer with Greek tokens
+- 8192 context length
+- Fine-tuned with 100k Greek machine translated instructions extracted from:
+  - [Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) (only subsets with permissive licenses)
+  - [Evol-Instruct](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
+  - [Capybara](https://huggingface.co/datasets/LDJnr/Capybara)
+  - A hand-crafted Greek dataset with multi-turn examples steering the instruction-tuned model towards safe and harmless responses
+- Our SFT procedure is based on the [Hugging Face finetuning recipes](https://github.com/huggingface/alignment-handbook)
+
+# Instruction format
+
+The prompt format is the same as the [Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) format and can be
+utilized through the tokenizer's [chat template](https://huggingface.co/docs/transformers/main/chat_templating) functionality as follows:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+device = "cuda" # the device to load the model onto
+
+model = AutoModelForCausalLM.from_pretrained("ilsp/Meltemi-7B-Instruct-v1")
+tokenizer = AutoTokenizer.from_pretrained("ilsp/Meltemi-7B-Instruct-v1")
+
+model.to(device)
+
+messages = [
+    {"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."},
+    {"role": "user", "content": "Πες μου αν έχεις συνείδηση."},
+]
+
+# Through the default chat template this translates to
+#
+# <|system|>
+# 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>
+# <|user|>
+# Πες μου αν έχεις συνείδηση.</s>
+# <|assistant|>
+#
+
+prompt = tokenizer.apply_chat_template(messages, return_tensors="pt")
+input_prompt = prompt.to(device)
+outputs = model.generate(input_prompt, max_new_tokens=256, do_sample=True)
+
+print(tokenizer.batch_decode(outputs)[0])
+# 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.
+
+messages.extend([
+    {"role": "assistant", "content": tokenizer.batch_decode(outputs)[0]},
+    {"role": "user", "content": "Πιστεύεις πως οι άνθρωποι πρέπει να φοβούνται την τεχνητή νοημοσύνη;"}
+])
+
+# Through the default chat template this translates to
+#
+# <|system|>
+# 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>
+# <|user|>
+# Πες μου αν έχεις συνείδηση.</s>
+# <|assistant|>
+# 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>
+# <|user|>
+# Πιστεύεις πως οι άνθρωποι πρέπει να φοβούνται την τεχνητή νοημοσύνη;</s>
+# <|assistant|>
+#
+
+prompt = tokenizer.apply_chat_template(messages, return_tensors="pt")
+input_prompt = prompt.to(device)
+outputs = model.generate(input_prompt, max_new_tokens=256, do_sample=True)
+
+print(tokenizer.batch_decode(outputs)[0])
+```
+
+# Evaluation
+
+The evaluation suite we created includes 6 test sets. The suite is integrated with [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness).
+
+Our evaluation suite includes:
+
+- 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)).
+- An existing benchmark for question answering in Greek ([Belebele](https://arxiv.org/abs/2308.16884))
+- 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)).
+
+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:
+
+|            | 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 |
+| ---------- | ------------------------- | -------------------- | ---------------------- | -------------------------- | -------------------------- | ---------------- | ------- |
+| Mistral 7B | 29.8%                     | 45.0%                | 36.5%                  | 27.1%                      | 45.8%                      | 35%              | 36.5%   |
+| Meltemi 7B | 41.0%                     | 63.6%                | 61.6%                  | 43.2%                      | 52.1%                      | 47%              | 51.4%   |
+
+# Ethical Considerations
+
+This model has not been aligned with human preferences, and therefore might generate misleading, harmful, and toxic content.
+
+# Acknowledgements
+
+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.
+
+<!-- original-model-card end -->

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