初始化项目，由ModelHub XC社区提供模型

Model: k08200/gaon-1.7b-v2-instruct
Source: Original Platform

README.md

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+---
+language:
+  - ko
+  - en
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+  - korean
+  - from-scratch
+  - qwen3-architecture
+base_model:
+  - 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](https://huggingface.co/mradermacher)):
+[static](https://huggingface.co/mradermacher/gaon-1.7b-v2-instruct-GGUF) ·
+[imatrix](https://huggingface.co/mradermacher/gaon-1.7b-v2-instruct-i1-GGUF)
+**Live demo:** [KO↔EN translator in your browser (WebGPU)](https://k08200.github.io/gaon/demo/)
+
+## Usage
+
+```python
+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](https://github.com/k08200/gaon/blob/main/docs/TECH_REPORT.md) §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.