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-Instruct
+
+## Introduction
+Seed-Coder-8B-Instruct is an 8-billion-parameter model instruction-tuned specifically for code generation, code reasoning, and code understanding. It is built to empower developers with high-quality, efficient code assistance. It features:
+- Trained on a **massively curated corpus**, where **an LLM-based filter** is applied to select **high-quality real-world code**, **text-code alignment data**, and **synthetic datasets** — ensuring cleaner and more useful data compared to traditional heuristic-based curation.
+- Achieves superior performance across **code generation**, **bug fixing**, and **reasoning** tasks, rivaling or surpassing larger open-source code models.
+- **Instruction-tuned** to reliably follow user intents across a diverse range of coding and reasoning prompts.
+- Supports **long-context handling** up to 32K tokens, enabling processing of complex multi-file projects and detailed coding tasks.
+
+## 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 generate code using the Hugging Face `pipeline` API:
+
+```python
+import transformers
+import torch
+
+model_id = "ByteDance-Seed/Seed-Coder-8B-Instruct"
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+messages = [
+    {"role": "user", "content": "Write a quick sort algorithm."},
+]
+
+outputs = pipeline(
+    messages,
+    max_new_tokens=512,
+)
+print(outputs[0]["generated_text"][-1]["content"])
+```
+
+## Evaluation
+
+Seed-Coder-8B-Instruct demonstrates strong performance across a variety of coding benchmarks, showing:
+- Competitive or superior results compared to similarly sized open-source code models.
+- Robustness across different programming languages and domains.
+- Ability to understand, reason, and repair complex code snippets.
+
+For detailed results, please check our [📑 paper](https://arxiv.org/pdf/xxx.xxxxx).
+
+## Citation
+
+If you find our work helpful, feel free to give us a cite.
+
+```
+@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}, 
+}
+```