Update README.md

README.md

@@ -1,3 +1,74 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0  
+base_model:  
+- ByteDance-Seed/Seed-Coder-8B-Base  
+---
+
+# Seed-Coder-8B-Reasoning
+
+## Introduction
+**Seed-Coder-8B-Reasoning** is an 8-billion-parameter model further optimized for **code reasoning**, **problem-solving**, and **algorithmic thinking** tasks.  
+Built upon the strong base of Seed-Coder, it undergoes additional training in sandbox environments to significantly enhance its ability to tackle complex coding problems and competitions. It features:
+- Trained on a **massively curated corpus**, filtered using an **LLM-based method** to ensure high-quality real-world code, text-code alignment, and synthetic datasets.
+- **Sandbox fine-tuning** to specifically strengthen **multi-step reasoning**, **algorithm design**, and **competitive programming** capabilities.
+- Maintains **long-context handling** up to 32K tokens, enabling it to reason over extended problem descriptions and large input-output examples.
+
+## Requirements
+You will need to install the latest versions of `transformers` and `accelerate`:
+
+```bash
+pip install -U transformers accelerate
+```
+
+## Quickstart
+
+Here is a simple example demonstrating how to load the model and perform code generation using the Hugging Face `pipeline` API:
+
+```python
+import transformers
+import torch
+
+model_id = "ByteDance-Seed/Seed-Coder-8B-Reasoning"
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+messages = [
+    {"role": "user", "content": "Solve the following problem: Given an array of integers, find two numbers such that they add up to a specific target number."},
+]
+
+outputs = pipeline(
+    messages,
+    max_new_tokens=512,
+)
+print(outputs[0]["generated_text"][-1]["content"])
+```
+
+## Evaluation
+
+Seed-Coder-8B-Reasoning has been evaluated extensively on reasoning-intensive code benchmarks, showing:
+- Significant improvements on **competitive programming** datasets and coding challenges.
+- Enhanced ability to **break down complex problems**, **design correct algorithms**, and **produce efficient implementations**.
+- Strong generalization to unseen problems across multiple domains (math, strings, arrays, graphs, DP, etc.).
+
+For detailed results, please check our [📑 paper](https://arxiv.org/pdf/xxx.xxxxx).
+
+## Citation
+
+If you find our work helpful, please consider citing our work:
+
+```
+@article{zhang2025seedcoder,
+    title={Seed-Coder: Let the Code Model Curate Data for Itself},
+    author={Xxx},
+    year={2025},
+    eprint={2504.xxxxx},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL},
+    url={https://arxiv.org/abs/xxxx.xxxxx}, 
+}
+```