commit 8db905a5387fccce42bbee39c729daf9f8d66e73 Author: ModelHub XC Date: Mon May 11 03:30:32 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: ilsp/Llama-Krikri-8B-Instruct Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..67f5409 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,38 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text +arena_hard_el.jpg filter=lfs diff=lfs merge=lfs -text +arena_hard_en.jpg filter=lfs diff=lfs merge=lfs -text diff --git a/KriKri_Logo-eng_54307d80-ee25-49f9-9204-0ce774499fbc.svg b/KriKri_Logo-eng_54307d80-ee25-49f9-9204-0ce774499fbc.svg new file mode 100644 index 0000000..2af61d9 --- /dev/null +++ b/KriKri_Logo-eng_54307d80-ee25-49f9-9204-0ce774499fbc.svg @@ -0,0 +1,41 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..e594c6c --- /dev/null +++ b/README.md @@ -0,0 +1,211 @@ +--- +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 the model's weights are getting reuploaded!* 🚨 + +# Llama-Krikri-8B-Instruct: An Instruction-tuned Large Language Model for the Greek language + +
+ Krikri +
+ +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) + + + + +# 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. + + +# 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 + +In the table below, we report the scores for our chat evaluation suite which includes: +- [Greek IFEval](https://huggingface.co/datasets/ilsp/ifeval_greek) (strict average) +- [English IFEval](https://huggingface.co/datasets/google/IFEval) (strict average) +- [Greek MT-Bench](https://huggingface.co/datasets/ilsp/mt-bench-greek) using gpt-4o-2024-08-06 as the judge model. +- [English MT-Bench](https://huggingface.co/datasets/HuggingFaceH4/mt_bench_prompts) using gpt-4o-2024-08-06 as the judge model. + +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. +It also exhibits **the strongest chat capabilities in the Greek MT-Bench benchmark** (+0.28 compared to Aya Expanse 8B), while also being very competitive in the English variant of the MT-Bench benchmark. + +| | IFEval EL (strict avg) | IFEval EN (strict avg) | MT-Bench EL | MT-Bench EN | +|---------------- |---------------- |----------------- |------------|------------| +| Qwen 2.5 7B Instruct | 46.2% | 74.8% | 5.83 | **7.87** | +| EuroLLM 9B Instruct | 51.3% | 64.5% | 5.98 | 6.27 | +| Aya Expanse 8B | 50.4% | 62.2% | 7.68 | 6.92 | +| Meltemi 7B v1.5 Instruct | 32.7% | 41.2% | 6.25 | 5.46 | +| Llama-3.1-8B Instruct | 45.8% | 75.1% | 6.46 | 7.25 | +| **Llama-Krikri-8B Instruct** | **67.5%** | **82.4%** | **7.96** | 7.21 | + + +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). We report 2 scores for Arena-Hard-Auto: +- No Style Control: The original version of the benchmark. +- With Style Control: The benchmark with style control methods for Markdown elements. You can read more about the methodology and technical background in this [blogspot](https://lmsys.org/blog/2024-08-28-style-control/). + +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). + +Llama-Krikri-8B Instruct exhibits very strong chat capabilities by scoring **higher than models over 8 times its size** (such as Llama-3.1-70B Instruct) and is also **competitive with closed-source** (e.g., GPT-4o-Mini) and **highly-performant open-source models** (e.g., Gemma 2 27B IT & Aya Expanse 32B). +![image/png](arena_hard_el.png) + +Below, we show the scores for the original Arena-Hard-Auto dataset for various open and closed chat models. We followed the original methodology by using **gpt-4-1106-preview as the judge model** and **gpt-4-0314 as the baseline model**. + +Llama-Krikri-8B Instruct performs very well in the English variant of Arena-Hard-Auto as well, since we can observe that it is **competitive with similarly sized LLMs** and that it **improves upon Llama-3.1-8B Instruct by +24.5% / +16%** (No style control / With style control). +![image/png](arena_hard_en.png) + +***Please note** that judge models are biased towards student models trained on distilled data from them. You can read more [here](https://arxiv.org/pdf/2502.01534?). + +**If you want to dig deeper, read more in our [paper](https://arxiv.org/abs/2505.13772).** + + +# 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. + +# Citation + +``` +@misc{roussis2025krikriadvancingopenlarge, + title={Krikri: Advancing Open Large Language Models for Greek}, + author={Dimitris Roussis and Leon Voukoutis and Georgios Paraskevopoulos and Sokratis Sofianopoulos and Prokopis Prokopidis and Vassilis Papavasileiou and Athanasios Katsamanis and Stelios Piperidis and Vassilis Katsouros}, + year={2025}, + eprint={2505.13772}, + archivePrefix={arXiv}, + primaryClass={cs.CL}, + url={https://arxiv.org/abs/2505.13772}, +} +``` \ No newline at end of file diff --git a/arena_hard_el.png b/arena_hard_el.png new file 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"bos_token": "<|begin_of_text|>", + "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n", + "clean_up_tokenization_spaces": true, + "cls_token": "<|begin_of_text|>", + "eos_token": "<|eot_id|>", + "extra_special_tokens": {}, + "model_input_names": [ + "input_ids", + "attention_mask" + ], + "model_max_length": 131072, + "pad_token": "<|reserved_special_token_247|>", + "sep_token": "<|eot_id|>", + "tokenizer_class": "PreTrainedTokenizerFast" +}