Improve model card: Add pipeline tag, library name, project links, and usage example

This PR enhances the model card by:
- Adding `pipeline_tag: text-generation` for better discoverability.
- Specifying `library_name: transformers` for clearer integration with the Hugging Face Transformers library.
- Including a link to the project page for additional information.
- Providing a direct Python usage example for quick inference.

README.md

@@ -1,20 +1,75 @@
 ---
 base_model:
 - meta-llama/Llama-2-70b-hf
-base_model_relation: quantized
 license: llama2
+base_model_relation: quantized
+pipeline_tag: text-generation
+library_name: transformers
 ---
-# Model Card
 
-- Base model: `meta-llama/Llama-2-70b-hf`
-- Quantization method: SqueezeLLM
-- Target bit-width: 4
-- Backend kernel: Any-Precision-LLM kernel (`ap-gemv`)
-- Calibration data: RedPajama (1024 sentences / 4096 tokens)
-- Calibration objective: Next-token prediction
+# GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance
+
+This repository contains a quantized version of `meta-llama/Llama-2-70b-hf` using the **GuidedQuant** method. GuidedQuant is a novel quantization approach that integrates gradient information from the end loss into the quantization objective while preserving cross-weight dependencies within output channels. It consistently boosts the performance of state-of-the-art quantization methods across weight-only scalar, weight-only vector, and weight-and-activation quantization. Additionally, it introduces a novel non-uniform scalar quantization algorithm, **LNQ**, which is guaranteed to monotonically decrease the quantization objective value, and outperforms existing methods in this category.
+
+- **Paper**: [GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance](https://arxiv.org/abs/2505.07004)
+- **Project Page**: [https://jusjinuk.me/blog/guidedquant/](https://jusjinuk.me/blog/guidedquant/)
+- **Code**: [https://github.com/snu-mllab/GuidedQuant](https://github.com/snu-mllab/GuidedQuant)
+
+### Model Details
+
+- **Base model**: `meta-llama/Llama-2-70b-hf`
+- **Quantization method**: SqueezeLLM (with GuidedQuant enhancement)
+- **Target bit-width**: 4
+- **Backend kernel**: Any-Precision-LLM kernel (`ap-gemv`)
+- **Calibration data**: RedPajama (1024 sentences / 4096 tokens)
+- **Calibration objective**: Next-token prediction
+
+## How to Use
+
+You can easily load and test this quantized model using the `AnyPrecisionForCausalLM` class, as shown in the following example (runs on one RTX 3090).
+
+```python
+from any_precision.modules.AnyPrecisionForCausalLM import AnyPrecisionForCausalLM
+from transformers import AutoTokenizer, TextStreamer
+import torch
+
+# This specific model within the repository (adjust if the model ID on the Hub is different):
+quantized_model_name = "jusjinuk/Llama-2-70b-hf-4bit-guidedquant-lnq"
+# Use float16 for Llama models, and bfloat16 for Qwen / Gemma models
+dtype = torch.float16 if "llama" in quantized_model_name.lower() else torch.bfloat16
+
+model = AnyPrecisionForCausalLM.from_quantized(quantized_model_name, torch_dtype=dtype)
+tokenizer = AutoTokenizer.from_pretrained(quantized_model_name)
+streamer = TextStreamer(tokenizer)
+
+prompt = "Write me a short and concise story about Harry, Ron, and Hermione.
+"
+chat = [
+    {"role": "system", "content": "You are a helpful assistant.
+"},
+    {"role": "user", "content": prompt},
+]
+
+inputs = tokenizer.apply_chat_template(
+    chat, tokenize=True, return_tensors="pt", add_generation_prompt=True
+).to(model.device)
+
+model.generate(inputs,
+    max_new_tokens=200, do_sample=False, temperature=1.0, streamer=streamer, pad_token_id=tokenizer.eos_token_id
+)
+```
+
+For detailed installation instructions and advanced usage (e.g., inference speed-up, different quantization types, evaluation), please refer to the [official GitHub repository](https://github.com/snu-mllab/GuidedQuant).
+
+## Citation
 
-# How to run
-- Follow the instruction in https://github.com/snu-mllab/GuidedQuant.
+Please cite our paper if you find our work useful:
 
-# References
-- [Model Paper](https://arxiv.org/abs/2505.07004)
+```bibtex
+@inproceedings{kim2025guidedquant,
+      title={GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance},
+      author={Jinuk Kim and Marwa El Halabi and Wonpyo Park and Clemens JS Schaefer and Deokjae Lee and Yeonhong Park and Jae W. Lee and Hyun Oh Song},
+      booktitle = {International Conference on Machine Learning (ICML)},
+      year={2025},
+}
+```