Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+base_model: 1bitLLM/bitnet_b1_58-3B
+tags:
+- text-generation
+- ternary
+- quantized
+- edge-ai
+- on-device
+language:
+- en
+library_name: vlut.cpp
+pipeline_tag: text-generation
+---
+
+# bitnet_b1_58-3B-vlut-gguf
+
+This repository contains **state-of-the-art ternary-packed versions** of [bitnet_b1_58-3B](https://huggingface.co/1bitLLM/bitnet_b1_58-3B) in GGUF format, optimized for efficient on-device inference using the [Vec-LUT](https://arxiv.org/abs/2512.06443) method.
+
+### Key Features
+
+- **🎯 SOTA Compression**: Achieves BPW (bits per weight) as low as **1.60** through **lossless** sub-2-bit ternary packing.
+- **⚡ SOTA Performance**: Delivers superior throughput (**4.2x speedup**) in **parallel inference** scenarios via vector lookup table (LUT).
+- **🔌 Drop-in Ready**: Seamless integration with [vlut.cpp](https://github.com/Cipherxzc/vlut.cpp) for immediate deployment on edge devices.
+
+## Available Model Variants
+
+Models are named as `ggml-model-{PACKING}_{TILE}.gguf`:
+
+| File Name | Packing (BPW) | Tile Size | Comment |
+|---------|---------|--------|------|
+| `ggml-model-I1_V.gguf` | `I1_V` (1.60) | 1 | |
+| `ggml-model-I1_V_2.gguf` | `I1_V` (1.60) | 2 | Recommended |
+| `ggml-model-I2_V.gguf` | `I2_V` (2.00) | 1 | |
+| `ggml-model-I2_V_4.gguf` | `I2_V` (2.00) | 4 | Recommended |
+| `ggml-model-I2_V_8.gguf` | `I2_V` (2.00) | 8 | |
+
+### Selection Guide
+
+- **BPW vs. Speed**: `I1_V` achieves lower memory usage but may not always outperform `I2_V` in speed.
+- **Tiling Trade-off**: Tiled variants (tile size > 1) deliver higher throughput but require larger cache capacity.
+- **Starting Point**: Use `I1_V_2` or `I2_V_4` as a starting point.
+
+For detailed tiling parameter analysis, see [Evaluation.md](https://github.com/Cipherxzc/vlut.cpp/blob/master/evaluation/Evaluation.md#tiling-parameters) and the paper.
+
+## Usage
+
+### Prerequisites
+
+Install [vlut.cpp](https://github.com/Cipherxzc/vlut.cpp) (these models require vlut.cpp, **not** vanilla llama.cpp):
+
+```bash
+git clone https://github.com/Cipherxzc/vlut.cpp.git
+cd vlut.cpp
+cmake -B build && cmake --build build --config Release -j4
+```
+
+### Download & Run
+
+```bash
+# Download the recommended variant, e.g., I2_V_4
+hf download <repo_id> \
+  ggml-model-I2_V_4.gguf --local-dir ./models
+
+# Run parallel inference
+./build/bin/llama-batched \
+  -m ./models/ggml-model-I2_V_4.gguf \
+  -p "I believe the meaning of life is" \
+  -np 32 -n 16 -t 1 --temp 0.5 --repeat-penalty 1.5
+
+# Benchmark performance
+./build/bin/llama-bench \
+  -m ./models/ggml-model-I2_V_4.gguf \
+  -t 1 -p 128 -n 0
+```
+
+For comprehensive usage instructions, refer to the [vlut.cpp Quick Start Guide](https://github.com/Cipherxzc/vlut.cpp/blob/master/README.md#quick-start).
+
+## Citation
+
+If you use these models, please cite our [paper](https://arxiv.org/abs/2512.06443):
+
+```bibtex
+@article{li2025veclut,
+  title={Vec-LUT: Vector Table Lookup for Parallel Ultra-Low-Bit LLM Inference on Edge Devices},
+  author={Li, Xiangyu and Yin, Chengyu and Wang, Weijun and Wei, Jianyu and Cao, Ting and Liu, Yunxin},
+  journal={arXiv preprint arXiv:2512.06443},
+  year={2025},
+  url={https://arxiv.org/abs/2512.06443}
+}
+```