Update README.md

README.md

@@ -12,13 +12,13 @@ tags:
 - language modeling
 pipeline_tag: text-generation
 ---
-
 # Continuous Autoregressive Language Models
 
 [![Paper](https://img.shields.io/badge/Paper_📃-green)](https://arxiv.org/abs/2510.27688)
 [![GitHub](https://img.shields.io/badge/GitHub_🧑‍💻-blue)](https://github.com/shaochenze/calm)
 [![HuggingFace](https://img.shields.io/badge/HuggingFace_🤗-orange)](https://huggingface.co/collections/cccczshao/calm)
-[![Project Page](https://img.shields.io/badge/Project_Page_✍️-yellowgreen)](https://shaochenze.github.io/blog/2025/CALM/)
+[![Blog](https://img.shields.io/badge/Blog_✍️-yellowgreen)](https://shaochenze.github.io/blog/2025/CALM/)
+
 
 ## Model Description
 
@@ -26,42 +26,23 @@ Modern Large Language Models (LLMs) are constrained by a fundamental bottleneck:
 
 This is achieved through a two-stage process:
 
-1.  **A high-fidelity autoencoder** learns to compress K tokens into a single vector and reconstruct them with near-perfect accuracy.
-2.  **A continuous-domain language model** then performs autoregressive prediction in this vector space.
+1. **A high-fidelity autoencoder** learns to compress K tokens into a single vector and reconstruct them with near-perfect accuracy.
+2. **A continuous-domain language model** then performs autoregressive prediction in this vector space.
 
 ### Key Features
 
-*   🚀 **Ultra-Efficient by Design:** Dramatically improves training and inference efficiency by reducing the number of autoregressive steps by a factor of K.
-*   💡 **A New Scaling Axis:** Introduces a new scaling dimension for LLMs—**semantic bandwidth (K)**. Instead of just scaling parameters and data, you can now scale the amount of information processed in a single step.
-*   🛠️ **A Comprehensive Likelihood-Free Toolkit:** Operating in a continuous domain requires new tools. This repository provides the full suite of algorithms that make CALM possible:
-    *   **A Robust Autoencoder** to learn high-fidelity continuous representations of token chunks.
-    *   **Energy-Based Training**, a principled and likelihood-free method for generative modeling.
-    *   **BrierLM**, a new metric for calibrated, likelihood-free evaluation of language models.
-    *   **Temperature Sampling** for controlled, high-quality text generation using only a black-box sampler.
+* 🚀 **Ultra-Efficient by Design:** Dramatically improves training and inference efficiency by reducing the number of autoregressive steps by a factor of K.
+* 💡 **A New Scaling Axis:** Introduces a new scaling dimension for LLMs—semantic bandwidth (K). Instead of just scaling parameters and data, you can now scale the amount of information processed in a single step.
+* 🛠️ **A Comprehensive Likelihood-Free Toolkit:** Operating in a continuous domain requires new tools. This repository provides the full suite of algorithms that make CALM possible:
+  
+  * **A Robust Autoencoder** to learn high-fidelity continuous representations of token chunks.
+  * **Energy-Based Training**, a principled and likelihood-free method for generative modeling.
+  * **BrierLM**, a new metric for calibrated, likelihood-free evaluation of language models.
+  * **Temperature Sampling** for controlled, high-quality text generation using only a black-box sampler.
 
 ## How to use
 
-We provide scripts for training and evaluation in our [GitHub README](https://github.com/shaochenze/calm).
-
-### Sample Usage (Text Generation)
-
-You can explore the core implementation of **CALM** in the GitHub repository. We've made it easy to use CALM by including our custom code in the 🤗[Hugging Face model zoo](https://huggingface.co/collections/cccczshao/calm). Simply set `trust_remote_code=True` when loading the models through the Transformers library.
-
-```python
-from transformers import pipeline, AutoTokenizer
-import torch
-
-model_name = "cccczshao/CALM-M" # Example model from the collection
-pipe = pipeline(
-    "text-generation",
-    model_name,
-    tokenizer=AutoTokenizer.from_pretrained(model_name),
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-    trust_remote_code=True,
-)
-print(pipe("The key to life is", max_new_tokens=20, do_sample=True)[0]["generated_text"])
-```
+See our [GitHub README](https://github.com/shaochenze/calm), where we provide scripts for training and evaluation.
 
 ## Contact