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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 repo contains GGUF format model files for [ilsp's Meltemi 7B Instruct v1](https://huggingface.co/ilsp/Meltemi-7B-Instruct-v1).
+
+<!-- description end -->
+<!-- README_GGUF.md-about-gguf start -->
+### About GGUF
+
+GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
+
+Here is an incomplete list of clients and libraries that are known to support GGUF:
+
+* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
+* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
+* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
+* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
+* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
+* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
+* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
+* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
+* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
+* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
+
+
+<!-- compatibility_gguf start -->
+## Compatibility
+
+These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
+
+<!-- README_GGUF.md-provided-files start -->
+## Provided files
+
+| Name | Quant method | Bits/Floats | Size | Max RAM required | Use case |
+| ---- | ---- | ---- | ---- | ---- | ----- | 
+| [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 | 5 | 7.40 GB| 7.30 GB | very low quality loss - recommended |
+| [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 | 13.90 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 | 27.80 GB| 29.30 GB | very large, extremely low quality loss - not recommended |
+
+**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
+
+**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
+
+The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
+
+* LM Studio
+* LoLLMS Web UI
+* Faraday.dev
+
+### On the command line, including multiple files at once
+
+I recommend using the `huggingface-hub` Python library:
+
+```shell
+pip3 install huggingface-hub
+```
+
+Then you can download any individual model file to the current directory, at high speed, with a command like this:
+
+```shell
+huggingface-cli download SPAHE/Meltemi-7B-Instruct-v1-GGUF meltemi-7B-instruct-v1_q8_0.gguf --local-dir . --local-dir-use-symlinks False
+```
+
+<!-- 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 -->