Create README.md (#2)

- Create README.md (3568c07e520905915bf4d7793e5ede18a362b193)


Co-authored-by: XUELING LIU <[email protected]>

README.md

@@ -0,0 +1,59 @@
+---
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- code
+---
+
+<h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1>
+
+<p align="center">
+<img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png">
+</p>
+<p align="center">
+  <a href="https://opencodeinterpreter.github.io/">[🏠Homepage]</a> 
+  |
+  <a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[🛠️Code]</a> 
+</p>
+<hr>
+
+## Introduction
+OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities.
+
+## Model Usage
+### Inference
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+model_path="OpenCodeInterpreter-DS-33B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+model.eval()
+
+prompt = "Write a function to find the shared elements from the given two lists."
+inputs = tokenizer.apply_chat_template(
+        [{'role': 'user', 'content': prompt }],
+        return_tensors="pt"
+    ).to(model.device)
+outputs = model.generate(
+    inputs, 
+    max_new_tokens=1024,
+    do_sample=False,
+    pad_token_id=tokenizer.eos_token_id,
+    eos_token_id=tokenizer.eos_token_id,
+)
+print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
+```
+
+
+## Contact
+
+If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected]. 
+We're here to assist you!"