From d88ea5c1c0ab19ac75822bb79b66ad9e91ba37b2 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Thu, 18 Jun 2026 05:07:16 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: konantech/Konan-LLM-OND Source: Original Platform --- .gitattributes | 36 +++ README.md | 309 +++++++++++++++++++++++ config.json | 30 +++ generation_config.json | 8 + model-00001-of-00002.safetensors | 3 + model-00002-of-00002.safetensors | 3 + model.safetensors.index.json | 405 +++++++++++++++++++++++++++++++ tokenizer.json | 3 + tokenizer_config.json | 77 ++++++ 9 files changed, 874 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 config.json create mode 100644 generation_config.json create mode 100644 model-00001-of-00002.safetensors create mode 100644 model-00002-of-00002.safetensors create mode 100644 model.safetensors.index.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json 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..263c9a9 --- /dev/null +++ b/README.md @@ -0,0 +1,309 @@ +--- +library_name: transformers +license: apache-2.0 +pipeline_tag: text-generation +base_model: +- Qwen/Qwen3-4B-Base +tags: +- konanllm +language: +- ko +- en +--- +# Konan-LLM-OND + +## **Overview** + +**Konan-LLM-OND**, a large language model from Konan Technology Inc., is based on [Qwen3-4B-Base](https://huggingface.co/Qwen/Qwen3-4B-Base). It has been specifically optimized for the Korean language through vocabulary expansion, continual pre-training, and instruction tuning to enhance performance and efficiency. +* **Languages**: Primarily Korean, with support for English. +* **Key Features:** + * **Expanded Korean Vocabulary:** The model's vocabulary has been expanded with additional Korean tokens to improve tokenization efficiency. As a result, Konan-LLM-OND is approximately 30% more token-efficient with Korean input than Qwen3, leading to greater cost-effectiveness and processing speed. + * **Continual Pre-training**: The model underwent continual pre-training on a large-scale Korean corpus using an expanded vocabulary. This process enhanced its fundamental understanding and text generation capabilities in Korean. + * **Supervised Fine-Tuning (SFT):** The model was fine-tuned on a high-quality Korean instruction dataset to improve its ability to understand and execute a wide variety of real-world tasks. + +## Benchmark Results + +#### **Model Performance (＜ 5B)** + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelModel sizeKoreanEnglish
KMMLUHRM8KKo-IFEvalMMLUGSM8KIFEval
Konan-LLM-OND4.0B54.33%53.70%68.42%70.76%86.66%73.38%
EXAONE-3.5-2.4B-Instruct2.4B45.22%38.55%60.53%61.76%78.54%77.73%
kanana-1.5-2.1b-instruct-25052.1B38.14%34.14%55.99%55.25%74.83%64.60%
Midm-2.0-Mini-Instruct2.3B43.24%37.30%66.81%55.62%72.55%68.30%
Qwen3-4B(w/o reasoning)4.0B52.55%54.16%68.42%71.81%76.57%80.04%
gemma-3-4b-it4.3B40.10%43.88%69.15%61.25%83.24%78.28%
+ +#### **Model Performance (≥ 7B)** + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelModel sizeKoreanEnglish
KMMLUHRM8KKo-IFEvalMMLUGSM8KIFEval
Konan-LLM-OND4.0B54.33%53.70%68.42%70.55%86.66%73.38%
A.X-4.0-Light7.2B62.48%51.08%71.49%73.15%86.58%81.33%
EXAONE-3.5-7.8B-Instruct7.8B53.03%48.0266.81%71.43%89.46%79.85%
kanana-1.5-8b-instruct-25058.0B47.80%39.65%71.05%65.90%76.57%76.80%
Midm-2.0-Base-Instruct11.5B58.43%51.18%75.00%71.84%79.83%79.67%
Qwen3-8B(w/o reasoning)8.1B57.43%57.88%70.91%76.45%77.79%82.81%
+ +Note: +* The highest scores are shown in bold. + +## **Benchmark Setup** + +All benchmarks were executed using the following standardized environment. + +* **Evaluation Framework**: `lm-evaluation-harness v0.4.9` +* **Runtime & Hardware**: All models were served with `vLLM v0.9.2` on NVIDIA GPU. +* **Inference Mode**: For every benchmark, we invoked the `chat_completions` API, and scores were computed solely from the generated responses. + +#### **Metric Adjustments** + +* **KMMLU** was evaluated in a zero-shot setting using a CoT-style prompt modified from the `kmmlu_direct` task in lm-evaluation-harness, with enhanced preprocessing filters applied during evaluation. + +* **MMLU** was evaluated in a zero-shot setting using a CoT-style prompt modified from the `mmlu_generative` task in lm-evaluation-harness, with enhanced preprocessing filters applied during evaluation. + +* **GSM8K** was evaluated in a zero-shot setting using the original prompt format from lm-evaluation-harness, with enhanced preprocessing filters applied. + +* **HRM8K** was evaluated in a zero-shot setting using the original prompt and data format from lm-evaluation-harness, without any modifications. + +* **Ko-IFEval** was evaluated in a zero-shot setting using the original **IFEval** protocol, with the dataset sourced from [allganize/IFEval-Ko](https://huggingface.co/datasets/allganize/IFEval-Ko). + +#### **Evaluation Protocol** + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BenchmarkScoring MethodFew-shot
KMMLUexact_match0-shot CoT
HRM8Kmean of hrm8k_gsm8k, hrm8k_ksm, hrm8k_math, hrm8k_mmmlu, hrm8k_omni_math0-shot
Ko-IFEvalmean of prompt_level_strict_acc, inst_level_strict_acc, prompt_level_loose_acc, inst_level_loose_acc0-shot
MMLUexact_match0-shot CoT
GSM8Kflexible-extract0-shot
IFEvalmean of prompt_level_strict_acc, inst_level_strict_acc, prompt_level_loose_acc, inst_level_loose_acc0-shot
+ +## Quickstart + +**Konan-LLM-OND** is supported in `transformers v4.52.0` and later. +```bash +pip install transformers>=4.52.0 +``` + +The code example below shows you how to get the model to generate content based on given inputs. +```python +import torch +from transformers import AutoTokenizer, AutoModelForCausalLM + +model_name = "konantech/Konan-LLM-OND" +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype=torch.bfloat16, + device_map="auto", +) +model.eval() +tokenizer = AutoTokenizer.from_pretrained(model_name) + +messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "대한민국 수도는?"} +] + + +input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) + +with torch.no_grad(): + output = model.generate( + input_ids, + max_new_tokens=64, + do_sample=False, + ) + +len_input_prompt = len(input_ids[0]) +response = tokenizer.decode(output[0][len_input_prompt:], skip_special_tokens=True) +print(response) +# 대한민국 수도는 서울입니다. +``` + +## Citation +``` +@misc{Konan-LLM-OND-2025, + author = {Konan Technology Inc.}, + title = {Konan-LLM-OND}, + year = {2025}, + url = {https://huggingface.co/konantech/Konan-LLM-OND} +} +``` diff --git a/config.json b/config.json new file mode 100644 index 0000000..90a59b0 --- /dev/null +++ b/config.json @@ -0,0 +1,30 @@ +{ + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 172728, + "eos_token_id": 172730, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 2560, + "initializer_range": 0.02, + "intermediate_size": 9728, + "max_position_embeddings": 32768, + "max_window_layers": 36, + "model_type": "qwen3", + "num_attention_heads": 32, + "num_hidden_layers": 36, + "num_key_value_heads": 8, + "rms_norm_eps": 1e-06, + "rope_scaling": null, + "rope_theta": 1000000, + "sliding_window": null, + 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