Update README.md

README.md

@@ -1,6 +1,6 @@
 ---
 license: other
-license_name: tongyi-qianwen
+license_name: qwen
 license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
 language:
 - en
@@ -61,7 +61,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 
 prompt = "Give me a short introduction to large language model."
 messages = [
-    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
     {"role": "user", "content": prompt}
 ]
 text = tokenizer.apply_chat_template(
@@ -84,11 +84,25 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 
 ### Processing Long Texts
 
-To handle extensive inputs exceeding 32,768 tokens, we utilize [YARN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
-
-For deployment, we recommend using vLLM. Please refer to our [Github](https://github.com/QwenLM/Qwen2.5) for usage if you are not familar with vLLM.
+The current `config.json` is set for context length up to 32,768 tokens.
+To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
+
+For supported frameworks, you could add the following to `config.json` to enable YaRN:
+```json
+{
+  ...,
+  "rope_scaling": {
+    "factor": 4.0,
+    "original_max_position_embeddings": 32768,
+    "type": "yarn"
+  }
+}
+```
 
-**Note**: Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**. We advise adding the `rope_scaling` configuration only when processing long contexts is required.
+For deployment, we recommend using vLLM. 
+Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM.
+Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**. 
+We advise adding the `rope_scaling` configuration only when processing long contexts is required.
 
 ## Evaluation & Performance