update README.md

README.md

@@ -20,7 +20,7 @@ tags:
 
 ## Introduction
 
-Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers;  Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:
+Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:
 
 - Significantly improvements in **code generation**, **code reasoning** and **code fixing**. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o.
 - A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies.
@@ -34,10 +34,9 @@ Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (
 - Number of Layers: 28
 - Number of Attention Heads (GQA): 12 for Q and 2 for KV
 - Context Length: Full 32,768 tokens
-  - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
 - Quantization: GPTQ 4-bit
 
-For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-coder/), [GitHub](https://github.com/QwenLM/Qwen2.5-Coder), [Documentation](https://qwen.readthedocs.io/en/latest/), [Arxiv](https://arxiv.org/abs/2409.12186).
+For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/), [GitHub](https://github.com/QwenLM/Qwen2.5-Coder), [Documentation](https://qwen.readthedocs.io/en/latest/), [Arxiv](https://arxiv.org/abs/2409.12186).
 
 ## Requirements
 
@@ -89,31 +88,10 @@ 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
 
-Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5-coder/).
+Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/).
 
 For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
 
@@ -123,10 +101,10 @@ If you find our work helpful, feel free to give us a cite.
 
 ```
 @article{hui2024qwen2,
-  title={Qwen2. 5-Coder Technical Report},
-  author={Hui, Binyuan and Yang, Jian and Cui, Zeyu and Yang, Jiaxi and Liu, Dayiheng and Zhang, Lei and Liu, Tianyu and Zhang, Jiajun and Yu, Bowen and Dang, Kai and others},
-  journal={arXiv preprint arXiv:2409.12186},
-  year={2024}
+      title={Qwen2. 5-Coder Technical Report},
+      author={Hui, Binyuan and Yang, Jian and Cui, Zeyu and Yang, Jiaxi and Liu, Dayiheng and Zhang, Lei and Liu, Tianyu and Zhang, Jiajun and Yu, Bowen and Dang, Kai and others},
+      journal={arXiv preprint arXiv:2409.12186},
+      year={2024}
 }
 @article{qwen2,
       title={Qwen2 Technical Report}, 

config.json

@@ -41,11 +41,11 @@
   },
   "rms_norm_eps": 1e-06,
   "rope_theta": 1000000.0,
-  "sliding_window": 131072,
+  "sliding_window": 32768,
   "tie_word_embeddings": true,
   "torch_dtype": "float16",
   "transformers_version": "4.39.3",
   "use_cache": true,
   "use_sliding_window": false,
   "vocab_size": 151936
-}
+}