commit 612feaf79a1fae4e816609162f6bd5f2cb8b7cb5 Author: ModelHub XC Date: Wed Apr 29 04:08:52 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: ilsp/Llama-Krikri-8B-Base Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..faeff01 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,37 @@ +*.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 +llama-krikri-image.jpg filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..659ef71 --- /dev/null +++ b/README.md @@ -0,0 +1,151 @@ +--- +license: llama3.1 +language: +- el +- en +pipeline_tag: text-generation +library_name: transformers +tags: +- text-generation-inference +--- + +🚨 **THIS IS A BASE MODEL. CONSIDER USING [Krikri 8B Instruct](https://huggingface.co/ilsp/Llama-Krikri-8B-Instruct) FOR CHAT APPLICATIONS** 🚨 + +# Llama-Krikri-8B-Base: A large foundation Language Model for the Greek language + +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-Base, as well as an instruct version, [Llama-Krikri-8B-Instruct](https://huggingface.co/ilsp/Llama-Krikri-8B-instruct). + +![image/png](llama-krikri-image.jpg) + +# Model Information + +- 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**. + + +# How to use + +## With Transformers + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer + +device = "cuda" + +model = AutoModelForCausalLM.from_pretrained("ilsp/Llama-Krikri-8B-Base") +tokenizer = AutoTokenizer.from_pretrained("ilsp/Llama-Krikri-8B-Base") + +model.to(device) + +input_text = tokenizer("Ένα κρικρί διαφέρει απο ένα λάμα επειδή", return_tensors='pt').to(device) +outputs = model.generate(input_text['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-Base \ + --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, +) + +response = client.completions.create(model="ilsp/Llama-Krikri-8B-Base", + prompt="Η εκπαίδευση μεγάλων γλωσσικών μοντέλων περιλαμβάνει") +print(response.choices[0].text) +``` + +# Evaluation + +Below, we report improvements of Llama-Krikri-8B-Base over Llama-3.1-8B for Greek and English: +- **+10.8%** on Greek benchmarks +- **+0.8%** on English benchmarks + +Our evaluations for Llama-Krikri-8B-Base, Llama-3.1-8B, and Meltemi 7B v1.5 are performed in a few-shot setting, consistent with the settings in the [Open LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). + +## Greek Benchmarks + + +The evaluation suite we created for the Greek language includes 6 test sets. You can run the suite by cloning this [lighteval fork](https://github.com/LeonVouk/lighteval). + +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)). + +We can see that our continual pretraining methodology enhances performance across all Greek test sets by a **+10.8%** average improvement over the base model. 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 | +|----------------|----------------|-------------|--------------|------------------|-------------------|---------|---------| +| Meltemi 7B v1.5 | 42.2% | 61.0% | 53.8% | 40.0% | 49.0% | 41.2% | 47.9% | +| Llama-3.1-8B | 33.4% | 72.8% | 52.1% | 39.9% | 51.1% | 42.6% | 48.7% | +| Llama-Krikri-8B | **53.8%** | **82.7%** | **64.6%** | **49.4%** | **54.2%** | **52.0%** | **59.5%** | + + +## English Benchmarks + +We can also see that our training methodology not only mitigates catastrophic forgetting effectively, but also improves average performance across all English test sets by **+0.8%**. The results for the English test sets are shown in the following table: + +| | Winogrande (5-shot) | Belebele (5-shot) | HellaSwag (10-shot) | ARC-Challenge (25-shot) | TruthfulQA MC2 (0-shot) | MMLU (5-shot) | Average | +|----------------|----------------|-------------|--------------|------------------|-------------------|---------|---------| +| Meltemi 7B v1.5 | 73.4% | 77.7% | 79.6% | 54.1% | 40.5% | 56.9% | 63.7% | +| Llama-3.1-8B | **74.6%** | 71.5% | **82.0%** | **58.5%** | 44.2% | **66.2%** | 66.2% | +| Llama-Krikri-8B | 72.6% | **79.8%** | 80.7% | 57.8% | **44.8%** | 65.1% | **67.0%** | + +Please note that all evaluations were run with the latest version of lighteval, which has some differences from past versions. This is why we report different scores for Meltemi-7B-v1.5 + + +# 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. + +# 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 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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" +}