Improve model card: Add pipeline tag, library, license, paper, project page, and usage

This PR improves the model card for the `Llama-2-70b-hf-3bit-GuidedQuant-QTIP` model by adding key metadata and enhancing its content:

- Adds `pipeline_tag: text-generation` for better discoverability and to enable the text generation inference widget.
- Adds `library_name: transformers` to correctly identify the library used for loading and interacting with the model, ensuring better integration with the Hugging Face ecosystem.
- Corrects the license from `llama2` to `mit`, aligning with the explicit license stated in the original GuidedQuant GitHub repository for the project's artifacts.
- Updates the paper link to the official Hugging Face Papers page: [GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance](https://huggingface.co/papers/2505.07004).
- Adds a direct link to the project page: [https://jusjinuk.me/blog/guidedquant/](https://jusjinuk.me/blog/guidedquant/).
- Includes a clear Python usage example for quick inference, derived from the project's quick start guide.

README.md

@@ -1,21 +1,75 @@
 ---
 base_model:
 - meta-llama/Llama-2-70b-hf
+license: mit
 base_model_relation: quantized
-license: llama2
+pipeline_tag: text-generation
+library_name: transformers
 ---
-# Model Card
 
-- Base model: `meta-llama/Llama-2-70b-hf`
-- Quantization method: BlockLDLQ with GuidedQuant Hessian
-- Target bit-width: 3
-- Backend kernel: QTIP kernel (HYB variant)
-- Calibration data: RedPajama (1024 sentences / 4096 tokens)
-- Calibration objective: Next-token prediction
-- num_groups (for GuidedQuant Hessian): 2
+# GuidedQuant: Llama-2-70B
 
-# How to run
-- Follow the instruction in https://github.com/snu-mllab/GuidedQuant and https://github.com/Cornell-RelaxML/qtip
+This model is a 3-bit quantized version of `meta-llama/Llama-2-70b-hf` using **GuidedQuant**, a novel post-training quantization approach. GuidedQuant integrates gradient information from the end loss into the quantization objective while preserving cross-weight dependencies. This method consistently boosts the performance of state-of-the-art quantization techniques across various settings.
 
-# References
-- [Model Paper](https://arxiv.org/abs/2505.07004)
+The model was presented in the paper [**GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance**](https://huggingface.co/papers/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: BlockLDLQ with GuidedQuant Hessian
+-   Target bit-width: 3
+-   Backend kernel: QTIP kernel (HYB variant)
+-   Calibration data: RedPajama (1024 sentences / 4096 tokens)
+-   Calibration objective: Next-token prediction
+-   num_groups (for GuidedQuant Hessian): 2
+
+## Usage
+
+You can easily load and test this quantized model using the `AnyPrecisionForCausalLM` class, which integrates seamlessly with the Hugging Face `transformers` library.
+
+```python
+from any_precision.modules.AnyPrecisionForCausalLM import AnyPrecisionForCausalLM
+from transformers import AutoTokenizer, TextStreamer
+import torch
+
+quantized_model_name = "jusjinuk/Llama-3.3-70B-Instruct-2bit-GuidedQuant-LNQ" # Example model, replace with current model name if different
+# 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 more comprehensive instructions on installation, advanced usage, and reproduction of results, please refer to the [GuidedQuant GitHub repository](https://github.com/snu-mllab/GuidedQuant) and the [QTIP kernel repository](https://github.com/Cornell-RelaxML/qtip).
+
+## Citation
+
+Please cite our paper if you find our work useful:
+
+```
+@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},
+}
+```