Improve model card: Add metadata, paper link, code link, project blog link and usage instructions

This PR enriches the model card for `JacobiForcing_Coder_7B_v1` by adding:
- A link to the paper: [Fast and Accurate Causal Parallel Decoding using Jacobi Forcing](https://huggingface.co/papers/2512.14681).
- The `license: apache-2.0`.
- The `library_name: transformers` as the model is built on Transformers and uses its tokenizer and generation utilities.
- The `pipeline_tag: text-generation` to correctly classify the model's functionality.
- A link to the GitHub repository and project blog.
- A "Usage" section with code examples directly from the GitHub README to demonstrate how to use the model for inference.

This will significantly improve the discoverability and usability of the model.

README.md

@@ -1,3 +1,67 @@
+---
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Jacobi Forcing: Fast and Accurate Causal Parallel Decoding
+
+This repository contains the `JacobiForcing_Coder_7B_v1` model, presented in the paper [Fast and Accurate Causal Parallel Decoding using Jacobi Forcing](https://huggingface.co/papers/2512.14681).
+
 Base Model: [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
 
-Training Data (Jacobi trajectories): https://huggingface.co/datasets/JacobiForcing/OpenCodeInstruct_training_data_n32
+Training Data (Jacobi trajectories): https://huggingface.co/datasets/JacobiForcing/OpenCodeInstruct_training_data_n32
+
+Jacobi Forcing is a novel training technique that converts Large Language Models (LLMs) into native causal parallel decoders. This approach maintains the causal autoregressive backbone and addresses the AR-to-diffusion mismatch by training the model to handle noisy future blocks along its own Jacobi decoding trajectories.
+
+It achieves up to $4.5\times$ higher tokens-per-forward and $4\times$ wall-clock speedup on coding and math tasks, while retaining near-AR generation quality.
+
+You can find more details on the project blog: [Jacobi Forcing Blog](https://hao-ai-lab.github.io/blogs/jacobi-forcing/)
+The official code repository is available here: [GitHub Repository](https://github.com/hao-ai-lab/JacobiForcing)
+
+## Usage
+
+You can try the chatbot demo locally or use the provided Python inference code.
+
+### Local Chatbot Demo
+```bash
+# modify the script to use your local path
+streamlit run applications/jacobi_model_chat.py
+```
+
+### Inference with Code
+You can use our provided `eagenerate` function for speedup generation, similar to using `generate` from Hugging Face. Here is an example:
+
+```python
+from eagle.model.ea_model import EaModel
+from fastchat.model import get_conversation_template
+import torch
+
+# Assuming base_model_path and EAGLE_model_path are defined
+# For example:
+base_model_path = "Qwen/Qwen2.5-Coder-7B-Instruct"
+EAGLE_model_path = "JacobiForcing/JacobiForcing_Coder_7B_v1" # Or your local path to the weights
+
+model = EaModel.from_pretrained(
+    base_model_path=base_model_path,
+    ea_model_path=EAGLE_model_path,
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True,
+    device_map="auto",
+    total_token=-1 # Automatically configure draft tokens
+)
+model.eval()
+
+your_message="Hello"
+conv = get_conversation_template("vicuna") # Use appropriate conversation template for your base model
+conv.append_message(conv.roles[0], your_message)
+conv.append_message(conv.roles[1], None)
+prompt = conv.get_prompt()
+
+input_ids = model.tokenizer([prompt]).input_ids
+input_ids = torch.as_tensor(input_ids).cuda()
+
+output_ids = model.eagenerate(input_ids, temperature=0.5, max_new_tokens=512)
+output = model.tokenizer.decode(output_ids[0])
+print(output)
+```