README.md

---
license: llama3.1
language:
- el
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
base_model:
- ilsp/Llama-Krikri-8B-Base
---

🚨 **PLEASE USE THE OFFICIAL QUANTIZED VERSIONS: [GGUF](https://huggingface.co/ilsp/Llama-Krikri-8B-Instruct-GGUF) OR REQUEST A SPECIFIC ONE** 🚨

🚨 *There is no guarantee that you are using the latest improved versions from 3rd party quantizations as we have updated the model's weights* 🚨

# Llama-Krikri-8B-Instruct: An Instruction-tuned Large Language Model for the Greek language

<div align="center">
  <img src="https://huggingface.co/ilsp/Llama-Krikri-8B-Instruct/resolve/main/KriKri_Logo-eng_54307d80-ee25-49f9-9204-0ce774499fbc.svg?raw=true" width="60%" alt="Krikri" />
</div>

Following the release of [Meltemi-7B](https://huggingface.co/ilsp/Meltemi-7B-v1) on the 26th March 2024, we are happy to welcome Krikri to the family of ILSP open Greek LLMs.
Krikri is built on top of [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B), extending its capabilities for Greek through continual pretraining on a large corpus of high-quality and locally relevant Greek texts. We present **Llama-Krikri-8B-Instruct**, along with the base model, [Llama-Krikri-8B-Base](https://huggingface.co/ilsp/Llama-Krikri-8B-Base)

<!-- ![image/png](llama-krikri-image.jpg) -->


# Model Information

## Base Model

- Vocabulary extension of the Llama-3.1 tokenizer with Greek tokens
- 128k context length (approximately 80,000 Greek words)
- We extend the pretraining of Llama-3.1-8B with added proficiency for the Greek language, by utilizing a large training corpus. 
  * This corpus includes 56.7 billion monolingual Greek tokens, constructed from publicly available resources.
  * Additionaly, to mitigate catastrophic forgetting and ensure that the model has bilingual capabilities, we use additional sub-corpora with monolingual English texts (21 billion tokens) and Greek-English parallel data (5.5 billion tokens).
  * The training corpus also contains 7.8 billion math and code tokens.
  * This corpus has been processed, filtered, and deduplicated to ensure data quality and is outlined below:


| Sub-corpus   | # Tokens         | Percentage |
|-----------|------------------|------------|
| Greek     | 56.7 B   | 62.3 %      |
| English   | 21.0 B   | 23.1 %      |
| Parallel  |  5.5 B   | 6.0 %       |
| Math/Code |  7.8 B   | 8.6 %       |
| **Total** | 91 B   |  **100%**       |


Chosen subsets of the 91 billion corpus were upsampled resulting in a size of **110 billion tokens**.

## Instruct Model

Llama-Krikri-8B-Instruct is the result of post-training Llama-Kriki-8B-Base and features:
- Enhanced chat capabilities and instruction-following in both Greek and English.
- Document translation from Greek to English, French, German, Italian, Portuguese, Spanish and vice versa.
- Great performance on generation, comprehension, and editing tasks, such as summarization, creative content creation, text modification, entity recognition, sentiment analysis, etc.
- Domain-specifc expertise for specialized legal, financial, medical, and scientific applications.
- Retrieval-Augmented Generation (RAG) utilizing multiple documents with 128k context length. 
- Improved coding and agentic capabilities with correct formatting and tool use.
- Conversion or structured extraction (e.g., XML, JSON) in data-to-text & text-to-data settings.
- Analytical thinking and Chain-of-Thought (CoT) reasoning for problem-solving.

## Post-training Methodology

We used a multi-stage process in order to build Llama-Krikri-8B-Instruct which includes:
- 2-stage Supervised Fine-Tuning with a combination of Greek & English instruction-response pairs (& multi-turn conversations)
  - **Stage 1**: **856,946** instruction-response pairs (371,379 Greek + 485,567 English)
  - **Stage 2**: **638,408** instruction-response pairs (279,948 Greek + 358,460 English)
- Alignment with a combination of Greek & English preference triplets (Instruction - Chosen Response - Rejected Response)
  - **Length Normalized DPO**: **92,394** preference triplets (47,132 Greek + 45,262 English)

## Post-training Data Construction

To build the SFT & DPO data, we utilized various methodologies including:
- Collecting existing high-quality datasets such as [Tulu 3](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture), [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk), [MAGPIE Ultra](https://huggingface.co/datasets/argilla/magpie-ultra-v1.0), [Orca Agent Instruct](https://huggingface.co/datasets/microsoft/orca-agentinstruct-1M-v1), [IFEval Like Data](https://huggingface.co/datasets/argilla/ifeval-like-data), [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized), [NVIDIA HelpSteer2](https://huggingface.co/datasets/nvidia/HelpSteer2), [Intel Orca](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs), [UltraMedical](https://huggingface.co/datasets/TsinghuaC3I/UltraMedical-Preference), and other datasets focused on safety, truthfulness, and instruction-following.
- Translating various data into Greek using an in-house translation tool.
- Regenerating translated data and contrasting the translated with the regenerated responses (i.e., for creating preference triplets).
- Distilling (with the MAGPIE methodology) models which exhibit strong performance in Greek, such as [Gemma 2 27B IT](https://huggingface.co/google/gemma-2-27b-it).
- Scoring data with the [Skywork Reward Gemma 2 27B v0.2](https://huggingface.co/Skywork/Skywork-Reward-Gemma-2-27B-v0.2) Reward Model and filtering using rule-based filters.
- Creating data for sentence and document translation using high-quality parallel corpora mainly from [ELRC-SHARE](https://elrc-share.eu/).
- Synthetically extracting question-answer pairs and multi-turn dialogues from diverse sources such as Wikipedia, EUR-LEX, Greek School Books, and Kallipos.

# Evaluation

In the table below, we report the scores for [Greek IFEval](https://huggingface.co/datasets/ilsp/ifeval_greek) (strict) and [English IFEval](https://huggingface.co/datasets/google/IFEval) (strict) for various chat models that exhibit strong performance.

We can observe that *Llama-Krikri-8B-Instruct exhibits the strongest performance* in instruction following for both Greek and English across all the models we tested. In particular, it surpasses Llama-3.1-8B-Instruct by **+21.7%** and **+7.3%** on the Greek and English IFEval respectively.

|       | IFEval EL (strict) | IFEval EN (strict) | 
|---------------- |---------------- |-----------------| 
| Qwen 2.5 7B Instruct | 46.2% | 74.8% | 
| EuroLLM 9B Instruct | 51.3% | 64.5% | 
| Aya Expanse 8B | 50.4% | 62.2% | 
| Meltemi 7B v1.5 Instruct | 32.7% | 41.2% | 
| Llama-3.1-8B Instruct | 45.8% | 75.1% | 
| Llama-Krikri-8B Instruct | **67.5%** | **82.4%** |


We also used the [Arena-Hard-Auto](https://huggingface.co/datasets/lmarena-ai/arena-hard-auto-v0.1) automatic evaluation tool, as well the translated (and post-edited) version for Greek that is publicly available [here](https://huggingface.co/datasets/ilsp/m-ArenaHard_greek).

Below, we show the scores for the Greek version of Arena-Hard-Auto for various open and closed chat models that were determined using **gpt-4o-2024-08-06 as the judge model** and **gpt-4o-mini-2024-07-18 as the baseline model** (i.e., by default 50% score).
![image/png]()

**Please note** that [recent research](https://arxiv.org/pdf/2502.01534?) has shown that judge models are biased towards student models, i.e., models finetuned on distilled data from a stronger/larger teacher model. While post-training data of GPT-4o-Mini are undisclosed, it would be very reasonable to assume that it has been trained -at least partly- with GPT-4o serving as the teacher model and therefore that the **judge is biased towards the baseline model**.

Below, we show the scores for the original Arena-Hard-Auto dataset for various open and closed chat models. We followed the original methodology of using **gpt-4-1106-preview as the judge model** and **gpt-4-0314 as the baseline model**.
![image/png]()

🚨 **More information on post-training, methodology, and evaluation coming soon.** 🚨


# How to use

## With Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda"

model = AutoModelForCausalLM.from_pretrained("ilsp/Llama-Krikri-8B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("ilsp/Llama-Krikri-8B-Instruct")

model.to(device)

system_prompt = "Είσαι το Κρικρί, ένα εξαιρετικά ανεπτυγμένο μοντέλο Τεχνητής Νοημοσύνης για τα ελληνικα και εκπαιδεύτηκες από το ΙΕΛ του Ε.Κ. \"Αθηνά\"."
user_prompt = "Σε τι διαφέρει ένα κρικρί από ένα λάμα;"

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": user_prompt},
]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
input_prompt = tokenizer(prompt, return_tensors='pt').to(device)
outputs = model.generate(input_prompt['input_ids'], max_new_tokens=256, do_sample=True)

print(tokenizer.batch_decode(outputs)[0])
```

## With OpenAI compatible server via vLLM

```bash
vllm serve ilsp/Llama-Krikri-8B-Instruct \
  --enforce-eager \
  --dtype 'bfloat16' \
  --api-key token-abc123
```

Then, the model can be used through Python using:
```python
from openai import OpenAI

api_key = "token-abc123"
base_url = "http://localhost:8000/v1"

client = OpenAI(
    api_key=api_key,
    base_url=base_url,
)

system_prompt = "Είσαι ένα ανεπτυγμένο μεταφραστικό σύστημα που απαντάει με λίστες Python. Δεν γράφεις τίποτα άλλο στις απαντήσεις σου πέρα από τις μεταφρασμένες λίστες."
user_prompt = "Δώσε μου την παρακάτω λίστα με μεταφρασμένο κάθε string της στα ελληνικά: ['Ethics of duty', 'Postmodern ethics', 'Consequentialist ethics', 'Utilitarian ethics', 'Deontological ethics', 'Virtue ethics', 'Relativist ethics']"

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": user_prompt},
]

response = client.chat.completions.create(model="ilsp/Llama-Krikri-8B-Instruct",
                                          messages=messages,
                                          temperature=0.0,
                                          top_p=0.95,
                                          max_tokens=8192,
                                          stream=False)

print(response.choices[0].message.content)
# ['Ηθική καθήκοντος', 'Μεταμοντέρνα ηθική', 'Συνεπειοκρατική ηθική', 'Ωφελιμιστική ηθική', 'Δεοντολογική ηθική', 'Ηθική αρετών', 'Σχετικιστική ηθική']
```

# Evaluation

🚨 **Instruction following and chat capability evaluation benchmarks coming soon.** 🚨

# 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.