commit 8a94727adf106dbc7ff8655af42571042b3c550a Author: ModelHub XC Date: Sun Jul 5 12:31:18 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: KISTI-KONI/KONI-Llama3.1-8B-R-20250831 Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.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 diff --git a/README.md b/README.md new file mode 100644 index 0000000..3f978ea --- /dev/null +++ b/README.md @@ -0,0 +1,331 @@ +--- +license: llama3.1 +language: +- ko +- en +tags: +- text-generation +- pytorch +- llama3.1 +- KISTI +- KONI +- 8b +library_name: transformers +base_model: +- KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024 +--- + +# KONI-Llama3.1-8B-R-20250831 + +[**KONI (KISTI Open Neural Intelligence)**](https://huggingface.co/KISTI-KONI) is a large language model developed by the Korea Institute of Science and Technology Information (KISTI). Designed specifically for the scientific and technological domains, KONI excels in both Korean and English, making it an ideal tool for tasks requiring specialized knowledge in these areas. + + +
+ + + +
+ + + + +**KONI-Llama3.1-8B-R-20250831** is an 8B Korean reasoning–specialized model developed collaboratively by KISTI, OneLineAI, HAE-RAE, and ORACLE as part of the [**KO-REAson series**](https://huggingface.co/KOREAson). Built upon [**KONI-Llama3.1-8B-Instruct-20241024**](https://huggingface.co/KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024), it is an instruction-tuned variant optimized for Korean-centric reasoning with full English support. By leveraging the **Language-Mixed Chain-of-Thought strategy**—interleaving Korean and English during the reasoning stage—the model improves both consistency and accuracy in complex reasoning. It is designed to address a wide range of tasks, from science and technology queries to general knowledge, mathematics, and logical problem-solving. While fully maintaining the tokenizer, context length, and API compatibility of the base model, it further enhances performance through supervised fine-tuning (SFT) tailored for Korean reasoning and terminology preservation. + +## Key Features + +- **Korean-Centric Reasoning with English Support**: Optimized primarily for Korean reasoning tasks while providing full support for English, enabling robust bilingual usage. +- **Language-Mixed Chain-of-Thought**: Employs Language-Mixed Chain-of-Thought strategy that interleaves Korean and English during the thought process, improving both consistency and accuracy in complex reasoning. +- **Specialized in Science & Technology**: Trained with strong emphasis on scientific and technological domains, making it well-suited for expert-level queries in these areas. +- **Base Model**: Built upon [KONI-Llama3.1-8B-Instruct-20241024](https://huggingface.co/KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024), derived from the Llama-3.1-8B-Instruct lineage. +- **Alignment**: Enhanced through Supervised Fine-Tuning (SFT) on 260k Language-Mixed Chain-of-Thought (CoT) examples, tailored for bilingual(Korean/English) reasoning and terminology preservation. +- **Strengths**: Demonstrating substantial performance gains across diverse reasoning benchmarks, this model provides coherent and complex reasoning in both Korean and English, capable of addressing a broad spectrum of tasks such as science and technology queries, general knowledge, mathematics, and logical problem-solving. +- **Intended Use**: Designed for science and technology Q&A, mathematical and logical problem-solving, Korean document understanding, and as a reasoning backbone for agent systems. + +--- + +# KO-REAson + +[**KO-REAson**](https://huggingface.co/KOREAson) is a series of Korean-centric reasoning language models developed in collaboration with [OneLineAI](https://onelineai.com/), [KISTI-KONI](https://huggingface.co/KISTI-KONI), [HAE-RAE](https://huggingface.co/HAERAE-HUB) and ORACLE. + +We use the **Language-Mixed Chain-of-Thought (CoT)** approach, which allows the model to alternate between English and Korean during the “Think” stage of reasoning, preserving key Korean terms while leveraging English for logical scaffolding. + +Top-performing models of our series [KONI-7B-R-20250831 (KO-REAson-AX3_1-7B-0831)](https://huggingface.co/KISTI-KONI/KONI-7B-R-20250831) and [KO-REAson-7B-Q2_5-0831](https://huggingface.co/KoReason/KO-REASon-7B-Q2_5-0831) show performance comparable to models trained on closed-source datasets such as Exaone-Deep-7.8B. + + +

+ Model Comparison + + Average performance (Held-out-Ko) of open models trained on closed or open data.
+ (Our models are highlighted in green.) + +

+ +--- + +## Model Details + +The **KO-REAson-0831** family comes in six variants based on the base model used. + +
+ +| Model | Base | +| :--------------------------------------------------------------------------------------------: | :--------------------: | +| [KO-REAson-L3_1-8B-0831](https://huggingface.co/KoReason/KO-REASon-L3_1-8B-0831) | [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) | +| [KONI-Llama3.1-8B-R-20250831(KO-REAson-KL3_1-8B-0831)](https://huggingface.co/KISTI-KONI/KONI-Llama3.1-8B-R-20250831) | [KONI-Llama3.1-8B-Instruct-20241024](https://huggingface.co/KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024) | +| [KO-REAson-G3-4B-0831](https://huggingface.co/KoReason/KO-REASon-G3-4B-0831) | [Gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it) | +| [KONI-7B-R-20250831(KO-REAson-AX3_1-7B-0831)](https://huggingface.co/KISTI-KONI/KONI-7B-R-20250831) | [A.X-3.1-Light (≈7B)](https://huggingface.co/skt/A.X-3.1-Light) | +| [KO-REAson-K2505_8B-0831](https://huggingface.co/KoReason/KO-REASon-K2505_8B-0831) | [kanana-1.5-8b-instruct-2505](https://huggingface.co/kakaocorp/kanana-1.5-8b-instruct-2505) | +| [KO-REAson-7B-Q2_5-0831](https://huggingface.co/KoReason/KO-REASon-7B-Q2_5-0831) | [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) | +
+ +--- + +# Performance + +**Evaluation Datasets** + +The model's performance was evaluated across a total of 11 benchmarks, and the evaluation suite is divided into two parts: (You can check these benchmarks in [HAERAE-HUB/KoSimpleEval](https://huggingface.co/datasets/HAERAE-HUB/KoSimpleEval)) + +- **Held-in**: This set of benchmarks is used for routine monitoring of the model's performance during the training and ablation study phases. +- **Held-out**: This set is used only once to evaluate the final model after all training and ablations are complete. + +This separation is designed to prevent inadvertent overfitting to the benchmarks during the iterative training process and to provide a more accurate measure of the model's generalization capabilities. + +
+ +|**Category**|**Held-in**|**Held-out**| +|:---:|:---:|:---:| +|**General Knowledge**|KMMLU-Redux|KMMLU-HARD, KMMLU-Pro| +|**Reasoning**|MCLM|KSM, GPQA, AIME2024, AIME2025| +|**Korean-specific**|HAE-RAE Bench|CLIcK, KoBALT-700| + +
+ +**Comparison with models trained on public datasets** + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Models#InstancesMethodologyHeld-Out(Ko)Held-Out(En)Total
KONI-7B-R-20250831
(KO-REASon-AX3_1-7B-0831; Ours)		260kSFT44.6041.2043.30
KONI-Llama3.1-8B-R-20250831
(KO-REAson-KL3_1-8B-0831; Ours)		260kSFT40.1330.5743.66
KO-REASon-7B-Q2_5-0831
(Ours)		260kSFT45.1038.7549.95
Open Recipe (En)
OpenThinker3-7B1.2MSFT33.6055.5041.80
s1.1-7B1kSFT35.6023.4031.10
Llama-3.1-Nemotron-Nano-8B-v1>3MSFT & RL27.0044.1033.40
Open Recipe (Ko)
Ko-R1-14B45kSFT43.7046.3044.70
Ko-R1-7B45kSFT27.3036.1030.60
LLaMa-3.1-Ko-Reasoning-8B63kSFT17.707.7014.00
+ +
+ +**Held-out benchmark performance** + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelModel SizeGeneralReasoningKorean-SpecificAverage
(Held-out)		Average
(Held-out-Ko)
KMMLU-HARDKMMLU-ProKSMAIME 2024AIME 2025GPQACLIcKKoBALT-700
Llama-3.1-Nemotron-Nano-8B8.0321.4722.8947.0656.6743.3332.3234.549.2933.4527.05
Llama-3.1-Korean-Reasoning-8B-Instruct8.0314.9121.726.090.000.0023.2339.656.1413.9717.70
EXAONE-Deep-7.8B7.8240.9637.3570.8070.0063.3364.6554.2418.8652.5244.44
DeepSeek-R1-Distill-Qwen-7B7.620.0023.0056.0960.0040.0043.430.008.2928.8517.48
DeepSeek-R1-Distill-Llama-8B8.0323.2226.2629.9733.3320.0046.4639.0513.2928.9526.36
s1.1-7B7.6231.1637.7030.6016.6723.3330.3056.8421.8631.0635.63
OpenThinker3-7B7.6230.3126.2663.5966.6753.3346.4647.6910.1435.6330.60
Ko-R1-7B7.6128.4619.3151.6146.6733.3328.2832.484.7130.6127.31
KONI-Llama3.1-8B-R-20250831
(KO-REAson-KL3_1-8B-0831; Ours)		8.0344.6440.0837.9623.3330.0038.3856.3921.5730.5740.13
KONI-7B-R-20250831
(KO-REASon-AX3_1-7B-0831; Ours)		7.2645.5738.1352.8053.3333.3336.8762.8623.4343.2944.56
KO-REASon-7B-Q2_5-0831
(Ours)		7.2646.8144.9348.1143.3330.0042.9360.6525.0042.7245.10
+ +--- + +## Citation + +The paper will be released soon! + +If you use this model in your work, please cite it as follows: + +``` +@article{KISTI-KONI/KONI-Llama3.1-8B-R-20250831, + title={KISTI-KONI/KONI-Llama3.1-8B-R-20250831}, + author={KISTI, OneLine AI, HAE-RAE and Oracle}, + year={2025}, + url={https://huggingface.co/KISTI-KONI/KONI-Llama3.1-8B-R-20250831} +} +``` + + +## Contact + +For any questions contact us via the following email :) + +``` +(KISTI)yangdonghun3@kisti.re.kr +(OneLineAI/HAE-RAE)spthsrbwls123@yonsei.ac.kr +``` + + +## Acknowlegments +``` +This research is a collaborative project between KISTI, OneLine AI, HAE-RAE and Oracle to investigate open reciepes to build Korean Reasoning Models.
+This research was also supported by the Korea Institute of Science and Technology Information (KISTI) (No.(KISTI) K25L1M1C1), aimed at developing KONI (KISTI Open Neural Intelligence), a large language model specialized in science and technology. +This work also benefited from the resources and technical support provided by the National Supercomputing Center (KISTI). +``` \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..f17337e --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,114 @@ +{{- bos_token }} +{%- if custom_tools is defined %} + {%- set tools = custom_tools %} +{%- endif %} +{%- if not tools_in_user_message is defined %} + {%- set tools_in_user_message = true %} +{%- endif %} +{%- if not date_string is defined %} + {%- set date_string = "27 Aug 2024" %} +{%- endif %} +{%- if not tools is defined %} + {%- set tools = none %} +{%- endif %} + +{#- This block extracts the system message, so we can slot it into the right place. #} +{%- if messages[0]['role'] == 'system' %} + {%- set system_message = messages[0]['content']|trim %} + {%- set messages = messages[1:] %} +{%- else %} + {%- set system_message = "" %} +{%- endif %} + +{#- System message + builtin tools #} +{{- "<|start_header_id|>system<|end_header_id|>\n\n" }} +{%- if builtin_tools is defined or tools is not none %} + {{- "Environment: ipython\n" }} +{%- endif %} +{%- if builtin_tools is defined %} + {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}} +{%- endif %} +{{- "You are KONI, an AI assistant trained based on LlaMA3.1 and created by KISTI to be helpful and honest. Your knowledge spans a wide range of topics, allowing you to engage in substantive conversations and provide analysis on complex subjects. Below is an instruction that describes a task. Write a response that appropriately completes the request. If you don't know the answer, just say that you don't know.\n" }} +{{- "Cutting Knowledge Date: July 2024\n" }} +{{- "Today Date: " + date_string + "\n\n" }} +{%- if tools is not none and not tools_in_user_message %} + {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }} + {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }} + {{- "Do not use variables.\n\n" }} + {%- for t in tools %} + {{- t | tojson(indent=4) }} + {{- "\n\n" }} + {%- endfor %} +{%- endif %} +{{- system_message }} +{{- "<|eot_id|>" }} + +{#- Custom tools are passed in a user message with some extra guidance #} +{%- if tools_in_user_message and not tools is none %} + {#- Extract the first user message so we can plug it in here #} + {%- if messages | length != 0 %} + {%- set first_user_message = messages[0]['content']|trim %} + {%- set messages = messages[1:] %} + {%- else %} + {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }} +{%- endif %} + {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}} + {{- "Given the following functions, please respond with a JSON for a function call " }} + {{- "with its proper arguments that best answers the given prompt.\n\n" }} + {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }} + {{- "Do not use variables.\n\n" }} + {%- for t in tools %} + {{- t | tojson(indent=4) }} + {{- "\n\n" }} + {%- endfor %} + {{- first_user_message + "<|eot_id|>"}} +{%- endif %} + +{%- for message in messages %} + {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %} + {%- if (message.role == 'assistant') %} + {{- '<|start_header_id|>' + 'KONI' + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }} + {%- else %} + {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }} + {%- endif %} + {%- elif 'tool_calls' in message %} + {%- if not message.tool_calls|length == 1 %} + {{- raise_exception("This model only supports single tool-calls at once!") }} + {%- endif %} + {%- set tool_call = message.tool_calls[0].function %} + {%- if builtin_tools is defined and tool_call.name in builtin_tools %} + {{- '<|start_header_id|>KONI<|end_header_id|>\n\n' -}} + {{- "<|python_tag|>" + tool_call.name + ".call(" }} + {%- for arg_name, arg_val in tool_call.arguments | items %} + {{- arg_name + '="' + arg_val + '"' }} + {%- if not loop.last %} + {{- ", " }} + {%- endif %} + {%- endfor %} + {{- ")" }} + {%- else %} + {{- '<|start_header_id|>KONI<|end_header_id|>\n\n' -}} + {{- '{"name": "' + tool_call.name + '", ' }} + {{- '"parameters": ' }} + {{- tool_call.arguments | tojson }} + {{- "}" }} + {%- endif %} + {%- if builtin_tools is defined %} + {#- This means we're in ipython mode #} + {{- "<|eom_id|>" }} + {%- else %} + {{- "<|eot_id|>" }} + {%- endif %} + {%- elif message.role == "tool" or message.role == "ipython" %} + {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }} + {%- if message.content is mapping or message.content is iterable %} + {{- message.content | tojson }} + {%- else %} + {{- message.content }} + {%- endif %} + {{- "<|eot_id|>" }} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|start_header_id|>KONI<|end_header_id|>\n\n' }} +{%- endif %} diff --git a/config.json b/config.json new file mode 100644 index 0000000..e1acb90 --- /dev/null +++ b/config.json @@ -0,0 +1,35 @@ +{ + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": 128001, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 131072, + "mlp_bias": false, + "model_type": "llama", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pretraining_tp": 1, + "rms_norm_eps": 1e-05, + "rope_scaling": { + "factor": 8.0, + "high_freq_factor": 4.0, + "low_freq_factor": 1.0, + "original_max_position_embeddings": 8192, + "rope_type": "llama3" + }, + 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