update README & LICENSE

README.md

@@ -1,5 +1,6 @@
 ---
 license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct/blob/main/LICENSE
 language:
 - en
 base_model:
@@ -28,12 +29,13 @@ Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (
 **This repo contains the instruction-tuned 7B Qwen2.5-Coder model**, which has the following features:
 - Type: Causal Language Models
 - Training Stage: Pretraining & Post-training
-- Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias
+- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
 - Number of Parameters: 7.61B
 - Number of Paramaters (Non-Embedding): 6.53B
 - Number of Layers: 28
 - Number of Attention Heads (GQA): 28 for Q and 4 for KV
-- Context Length: 131,072 tokens
+- Context Length: Full 131,072 tokens
+  - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
   
 For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-coder/), [GitHub](https://github.com/QwenLM/Qwen2.5-Coder), and [Documentation](https://qwen.readthedocs.io/en/latest/).
 
@@ -85,7 +87,27 @@ generated_ids = [
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ```
 
+### Processing Long Texts
 
+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"
+  }
+}
+```
+
+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