language, license, tags, base_model, pipeline_tag, library_name, new_version
| language | license | tags | base_model | pipeline_tag | library_name | new_version | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
apache-2.0 |
|
Qwen/Qwen2-0.6B | text-generation | transformers | 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 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.