Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+<div align="center">
+
+<picture> 
+  <img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="120px">
+</picture>
+
+</div>
+
+<p align="center">
+  <a href="https://github.com/01-ai">🐙 GitHub</a> •
+  <a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
+  <a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
+  <a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a> 
+  <br/>
+  <a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
+  <a href="https://01-ai.github.io/">💪 Tech Blog</a> •
+  <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
+  <a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
+</p>
+
+# Intro
+
+Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters. 
+
+Key features:
+- Excelling in long-context understanding with a maximum context length of 128K tokens.
+- Supporting 52 major programming languages, including popular ones such as Java, Python, JavaScript, and C++.
+
+For model details and benchmarks, see [Yi-Coder blog](https://01-ai.github.io/) and [Yi-Coder README](https://github.com/01-ai/Yi-Coder).
+
+<p align="left"> 
+  <img src="https://github.com/01-ai/Yi/blob/main/assets/img/coder/demo1.gif?raw=true" alt="demo1" width="500"/> 
+</p>
+
+# Models
+
+| Name               | Type | Download                                                                                                                                          |
+|--------------------|------|---------------------------------------------------------------------------------------------------------------------------------------------------|
+| Yi-Coder-9B-Chat   | Chat | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-9B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-9B-Chat) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-9B-Chat)                   |
+| Yi-Coder-1.5B-Chat | Chat | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-1.5B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-1.5B-Chat) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-1.5B-Chat)                   |
+| Yi-Coder-9B        | Base | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-9B) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-9B) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-9B/)                   |
+| Yi-Coder-1.5B      | Base | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-1.5B) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-1.5B) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-1.5B)                   |
+|                    | 
+
+# Benchmarks
+
+As illustrated in the figure below, Yi-Coder-9B-Chat achieved an impressive 23% pass rate in LiveCodeBench, making it the only model with under 10B parameters to surpass 20%. It also outperforms DeepSeekCoder-33B-Ins at 22.3%, CodeGeex4-9B-all at 17.8%, CodeLLama-34B-Ins at 13.3%, and CodeQwen1.5-7B-Chat at 12%.
+
+<p align="left"> 
+  <img src="https://github.com/01-ai/Yi/blob/main/assets/img/coder/download1.png?raw=true" alt="download1" width="500"/> 
+</p>
+
+# Quick Start
+
+You can use transformers to run inference with Yi-Coder models (both chat and base versions) as follows:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+device = "cuda" # the device to load the model onto
+model_path = "01-ai/Yi-Coder-9B-Chat"
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto").eval()
+
+prompt = "Write a quick sort algorithm."
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=1024,
+    eos_token_id=tokenizer.eos_token_id  
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+For getting up and running with Yi-Coder series models quickly, see [Yi-Coder README](https://github.com/01-ai/Yi-Coder).