Instructions to use brandonbaek/Bori-1-0.6B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use brandonbaek/Bori-1-0.6B-Base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="brandonbaek/Bori-1-0.6B-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("brandonbaek/Bori-1-0.6B-Base")
model = AutoModelForCausalLM.from_pretrained("brandonbaek/Bori-1-0.6B-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use brandonbaek/Bori-1-0.6B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "brandonbaek/Bori-1-0.6B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "brandonbaek/Bori-1-0.6B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/brandonbaek/Bori-1-0.6B-Base

SGLang

How to use brandonbaek/Bori-1-0.6B-Base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "brandonbaek/Bori-1-0.6B-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "brandonbaek/Bori-1-0.6B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "brandonbaek/Bori-1-0.6B-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "brandonbaek/Bori-1-0.6B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use brandonbaek/Bori-1-0.6B-Base with Docker Model Runner:
```
docker model run hf.co/brandonbaek/Bori-1-0.6B-Base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

A newer version of this model is available: 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.

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Safetensors

Model size

0.7B params

Tensor type

F32

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Quantizations

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