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README.md

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+---
+license: apache-2.0
+language:
+  - ko
+  - en
+tags:
+  - korean
+  - reasoning
+  - instruction-tuning
+  - fine-tuning
+  - trillion
+  - llama
+  - sft
+---
+
+# 🧠 Trillion-7B-preview-Ko-Reasoning
+
+> A large-scale Korean reasoning model fine-tuned from **trillionlabs/Trillion-7B-preview**, designed to excel in logical and multi-hop reasoning tasks in Korean.
+
+---
+
+## 📌 Overview
+
+**Trillion-7B-preview-Ko-Reasoning** is a fine-tuned version of [trillionlabs/Trillion-7B-preview](https://huggingface.co/trillionlabs/Trillion-7B-preview), specifically optimized for **logical reasoning in Korean**. This model is part of a broader research initiative to explore:
+
+- The **transition from multilingual reasoning LLMs** to **Korean-specialized reasoning models**
+- The enhancement of **non-reasoning Korean language models** into **reasoning-capable variants**
+- The development of open-access models that rival proprietary alternatives in complex reasoning tasks
+
+This model was fine-tuned using a large-scale Korean-English instruction dataset containing diverse multi-hop questions, symbolic logic tasks, and human-crafted reasoning steps.
+
+---
+
+## 🧪 Benchmark Results
+
+> - 📊 All benchmarks were measured using the **0-shot CoT (Chain-of-Thought)** method.
+> - 📊 The **Score** represents either the **accuracy (%)** of correct answers or a rating on a **1-10 scale** from a judge model.
+> - 📊 **LLM-as-a-judge** benchmarks were evaluated using **GPT-4o (2024-08-01-preview)**.
+
+| **Benchmark**    | **Score**     |
+|------------------|---------------|
+| GPQA diamond     | 56.2          |
+| GSM8K            | 53.1          |
+| HAERAE           | 73.7          |
+| KSM              | 57.8          |
+| LogicKor         | 8.40          |
+| Math500          | 72.8          |
+| MT-Bench         | 7.90          |
+| MT-Bench(Ko)     | 7.87          |
+
+---
+
+## 🧑‍💻 Usage
+
+Install Transformers >= 4.50:
+
+```bash
+pip install -U transformers
+```
+
+Basic example:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "DimensionSTP/Trillion-7B-preview-Ko-Reasoning"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "서울과 부산 중 어디가 더 커?"
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=4096
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+---
+
+## 🧠 Base Model: trillionlabs/Trillion-7B-preview
+
+The base model, [trillionlabs/Trillion-7B-preview](https://huggingface.co/trillionlabs/Trillion-7B-preview), is a LLM developed by the Trillion Labs.
+For more technical details, refer to the [Trillion 7B Technical Report](https://arxiv.org/pdf/2504.15431).
+
+---
+
+## 🧱 Model Architecture
+
+| Property         | Value                  |
+|------------------|------------------------|
+| Architecture     | LlamaForCausalLM       |
+| Parameters       | 7B                     |
+| Context Length   | 4,096 tokens           |
+| Tokenizer        | LlamaTokenizer (BPE)   |
+
+---
+
+## 📅 Release Date
+
+**Mar 2025**  
+This model was released in March 2025 as part of the **Ko-Reasoning Series**, which focuses on pushing the boundaries of open-source reasoning in Korean using modern LLMs.
+
+---
+
+## 📬 Contact
+
+For questions, collaborations, or deployment inquiries, please contact:
+
+- 🤖 Hugging Face: [https://huggingface.co/DimensionSTP](https://huggingface.co/DimensionSTP)
+- ✉️ Email: [ddang8jh@gmail.com]
+
+---
+
+## 📦 Available Checkpoints
+
+- ✅ `main`: Final stable version from the `last` branch
+- ✅ All training artifacts available (tokenizer, config, model weights)