Add comprehensive model card for FR3E

This PR significantly enhances the model card by adding:
- A detailed description of the FR3E model based on the paper's abstract.
- Essential metadata including `pipeline_tag: text-generation`, `library_name: transformers`, and `tags: qwen`.
- A direct link to the associated paper: [First Return, Entropy-Eliciting Explore](https://huggingface.co/papers/2507.07017).
- A basic Python usage example demonstrating how to use the model with the `transformers` library for conversational text generation.

These improvements will make the model more discoverable, provide crucial context, and facilitate easier adoption for users on the Hugging Face Hub.

README.md

@@ -1,3 +1,81 @@
----
-license: mit
----
+---
+license: mit
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- qwen
+- llm
+- reinforcement-learning
+- reasoning
+---
+
+# FR3E (First Return, Entropy-Eliciting Explore)
+
+The FR3E (First Return, Entropy-Eliciting Explore) model is a novel structured exploration framework designed to enhance the reasoning abilities of Large Language Models (LLMs). It was presented in the paper [First Return, Entropy-Eliciting Explore](https://huggingface.co/papers/2507.07017).
+
+## Model Description
+
+FR3E addresses the challenges of unstable exploration in Reinforcement Learning from Verifiable Rewards (RLVR) by identifying high-uncertainty decision points within reasoning trajectories. It then performs targeted rollouts to construct semantically grounded intermediate feedback, providing precise guidance without the need for dense supervision.
+
+Empirical results on mathematical reasoning benchmarks (AIME24) demonstrate that FR3E promotes more stable training, produces longer and more coherent responses, and significantly increases the proportion of fully correct trajectories. This framework highlights an effective approach to improving LLM reasoning through robust and structured exploration.
+
+## Paper
+
+For more detailed information, please refer to the research paper:
+[**First Return, Entropy-Eliciting Explore**](https://huggingface.co/papers/2507.07017)
+
+## Project Page
+
+You can find more information about the project on its Hugging Face organization page:
+[**FR3E-Bytedance**](https://huggingface.co/FR3E-Bytedance)
+
+## Usage
+
+You can use the FR3E model with the Hugging Face `transformers` library. This model is based on the Qwen2 architecture and can be loaded as a causal language model for text generation tasks, especially in a conversational format.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Replace "FR3E-Bytedance/FR3E-Qwen2-7B-Instruct" with the specific model ID if different
+model_id = "FR3E-Bytedance/FR3E-Qwen2-7B-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "What is the capital of France?"},
+]
+
+# Apply chat template and tokenize
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+# Generate response
+outputs = model.generate(input_ids, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.9)
+
+# Decode the generated text, skipping the input prompt
+generated_text = tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True)
+print(generated_text)
+
+# Example with a reasoning prompt
+reasoning_messages = [
+    {"role": "user", "content": "Solve the following problem step-by-step: A rectangular garden has a length of 15 meters and a width of 10 meters. If you want to put a fence around it, and the fencing costs $5 per meter, how much will it cost to fence the entire garden?"},
+]
+
+reasoning_input_ids = tokenizer.apply_chat_template(
+    reasoning_messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+reasoning_outputs = model.generate(reasoning_input_ids, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.9)
+reasoning_generated_text = tokenizer.decode(reasoning_outputs[0][len(reasoning_input_ids[0]):], skip_special_tokens=True)
+print(reasoning_generated_text)
+```