Update README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+- ko
+- ja
+- zh
+---
+
+# Tri-1.8B-Base
+
+
+Tri-0.5B-Base is a 1.8 billion parameter multilingual language model trained as an **early experimental run** before the Tri-7B training.
+
+The model covers **English, Korean, Japanese, and Chinese**, with additional exposure to programming languages and mathematical reasoning.
+Pretrained on \~1.88 trillion tokens, it serves as a lightweight base model for research, fine-tuning, and open-source community use - especially for advancing Korean LLM development.
+
+
+## Model Summary
+
+
+* Architecture: decoder-only Transformer (LLaMA-style)
+* Parameters: \~1.8B (untied embeddings and LM head)
+* Layers / hidden size / attention heads: 25 / 2048 / 16
+* Feedforward hidden size: 5,632 (SiLU-gated MLP)
+* Context length: 4,096
+* RoPE θ: 100,000
+* Training precision: bfloat16
+* Status: base pretraining only (no instruction tuning, no RLHF)
+
+
+## Intended Use
+
+* As a **foundation** for downstream fine-tuning and alignment.
+* Research on multilingual pretraining and adaptation.
+
+
+## Limitations
+
+* Being a base model, outputs may be unsafe, incoherent, or factually incorrect.
+
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+name = "trillionlabs/Tri-1.8B-Base"
+tok = AutoTokenizer.from_pretrained(name)
+model = AutoModelForCausalLM.from_pretrained(
+    name,
+    torch_dtype="bfloat16",
+    device_map="auto"
+)
+
+prompt = "Write a short paragraph about Hangul."
+x = tok(prompt, return_tensors="pt").to(model.device)
+y = model.generate(
+    **x,
+    max_new_tokens=128,
+    do_sample=True,
+    temperature=0.8,
+    top_p=0.95
+)
+print(tok.decode(y[0], skip_special_tokens=True))
+```
+
+## License
+
+This model is released under the **Apache 2.0 License**.
+See [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) for details.
+
+---
+
+## Citation
+
+If you use this model, please cite it as:
+
+```
+@misc{trillionlabs_tri18b_base_2025,
+  title  = {Tri-1.8B-Base},
+  author = {Trillion Labs},
+  year   = {2025},
+  note   = {https://huggingface.co/trillionlabs/Tri-1.8B-Base}
+}
+```