Update README.md

README.md

@@ -1,3 +1,65 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- chat
+- coding
+base_model: Qwen/Qwen2-7B
+datasets:
+- motexture/cData
+---
+
+# Cwen-7B-Instruct
+
+## Introduction
+
+Cwen-7B-Instruct is a fine-tuned version of Qwen2-7B-Instruct, optimized using the cData coding dataset to enhance its coding capabilities across various languages, with a primary focus on low-level ones.<br>
+
+## Quickstart
+
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cuda" # the device to load the model onto
+
+model = AutoModelForCausalLM.from_pretrained(
+    "motexture/Cwen-7B-Instruct",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("motexture/Cwen-7B-Instruct")
+
+prompt = "Write a C++ program that demonstrates the concept of separate compilation and linkage using namespaces and header files. The program should consist of multiple source files, each containing a portion of the program's code, and a header file that contains the interface information for the program.\n\nThe program should define a namespace my_namespace that contains a class  MyClass with a member function print() that takes an integer as an argument. The program should also define a function main() that uses an object of the MyClass class to print a message.\n\nThe program should be compiled and linked separately, with each source file being compiled individually and then linked together to form the final executable."
+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=512
+)
+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]
+```
+
+## Citation
+
+```
+@article{qwen2,
+  title={Qwen2 Technical Report},
+  year={2024}
+}
+```