language, license, pipeline_tag, tags, base_model

language	license	pipeline_tag	tags	base_model
ko en	apache-2.0	text-generation	korean from-scratch qwen3-architecture	Qwen/Qwen3-1.7B

Gaon-1.7B v2 Instruct (가온)

A bilingual (Korean + English) 1.7B chat model trained entirely from scratch — architecture, data pipeline, pretraining, and instruction tuning — by one person on borrowed idle GPUs, for $0. Gaon is pure Korean for "center/core."

• Architecture: Qwen3-1.7B-compatible (28L, hidden 2048, GQA 16Q/8KV, QK-Norm, SwiGLU, tied embeddings, vocab 151,936). Loads with Qwen3ForCausalLM.
• Pretraining: 34B tokens (FineWeb-Edu EN + FineWeb-2 KO + ~20% Python code), 4× B200 FSDP, final loss 1.96.
• Instruction tuning: sequence-level distillation from Qwen2.5-7B-Instruct (Apache-2.0), ~9k KO/EN instructions, SFT loss 1.26.
• Code & full tech report: https://github.com/k08200/gaon

Run locally: ollama run hf.co/k08200/gaon-1.7b-v2-instruct-GGUF — or community GGUF quants (12 sizes, 516 MB – 3.4 GB, by @mradermacher): static · imatrix Live demo: KO↔EN translator in your browser (WebGPU)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

m = "k08200/gaon-1.7b-v2-instruct"
tok = AutoTokenizer.from_pretrained(m)
model = AutoModelForCausalLM.from_pretrained(m, torch_dtype=torch.bfloat16).eval()

msgs = [{"role": "user", "content": "한국의 수도는 어디인가요?"}]
prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
enc = tok(prompt, return_tensors="pt")
out = model.generate(**enc, max_new_tokens=200, do_sample=True, temperature=0.7)
print(tok.decode(out[0][enc.input_ids.shape[1]:], skip_special_tokens=True))

Benchmarks

Base model (v2), lm-eval-harness, 0-shot, same settings for both:

Benchmark	Gaon-1.7B v2	Qwen3-1.7B-Base	Random
MMLU (English knowledge)	25.1	62.6	25.0
KMMLU (Korean knowledge)	22.3	35.5	25.0
HAERAE (Korean culture/lexis)	19.9	46.8	~20
KoBEST (Korean understanding)	51.5	—	~50

This is the most honest number in the project: Gaon is fluent in two languages, follows instructions, and translates, yet scores at chance on knowledge — because Qwen3-1.7B (same architecture, same size) trained on ~36T tokens, roughly 1000× our 34B. Linguistic competence emerges in tens of billions of tokens; world knowledge needs trillions. A continued-pretraining run (96B Korean-heavy tokens) lifts KMMLU to ~27–29 then plateaus — see the tech report §6.

Honest limitations

A from-scratch credential and research artifact, not a frontier competitor. Knowledge benchmarks at chance level (above); coding and strict-format tasks are weak. Korean/English chat, explanation, and simple translation work well. Best used as a reproducible small-LLM pipeline and a base for vertical fine-tuning on domain data.