初始化项目，由ModelHub XC社区提供模型

Model: brandonbaek/Bori-1-0.6B-Base
Source: Original Platform

README.md

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+---
+language:
+- ko
+- en
+license: apache-2.0
+tags:
+- slm
+- korean
+- experimental
+- qwen2
+- text-generation
+- continuous-pre-training
+base_model: Qwen/Qwen2-0.6B
+pipeline_tag: text-generation
+library_name: transformers
+new_version: brandonbaek/Bori-2-135M-Base
+---
+
+# 🌾 Bori-1 0.6B Base (Checkpoint 1000)
+
+> **🚀 Newer Version Available:** The Bori project has evolved! Please see [Bori-2 135M Base](https://huggingface.co/brandonbaek/Bori-2-135M-Base) for the completed Phase 2 pre-training pipeline, or check out the Bori GitHub repository for the latest **Bori-3** developments.
+
+**Bori-1** is the very first experimental proof-of-concept for the Bori project, aimed at exploring the feasibility of training bilingual (Korean-English) Small Language Models (SLMs) under extreme compute constraints.
+
+⚠️ **Status**: This was a preliminary exploratory run paused early at **Checkpoint 1000**. It is published solely for historical tracking and to serve as a baseline for the architectural shifts made in Bori-2 and Bori-3.
+
+## 🤖 Model Details
+- **Base Architecture**: Qwen2
+- **Parameter Count**: ~600M
+- **Languages**: Korean, English
+
+## 💻 Hardware & Compute
+- **Hardware**: Trained on Kaggle Notebooks using **2x NVIDIA T4 GPUs** (16GB VRAM each).
+- **Constraints**: Navigating the memory constraints of a 600M parameter model on 16GB GPUs without advanced quantization required aggressive gradient accumulation and small batch sizes, leading to the decision to pivot to the highly efficient ~135M architecture for Bori-2 to allow for more robust pre-training experimentation.
+
+## ⚠️ Limitations & Intended Use
+This model was paused very early in its training lifecycle. It is significantly undertrained and exhibits poor coherence. It should not be used for text generation, fine-tuning, or deployment. Its primary value is as a historical artifact demonstrating the early stages of the Bori project's development.