Update README.md

README.md

@@ -2,6 +2,9 @@
 tags:
 - fp8
 - vllm
+license: other
+license_name: deepseek-license
+license_link: LICENSE
 ---
 
 # DeepSeek-Coder-V2-Lite-Instruct-FP8
@@ -19,13 +22,13 @@ tags:
 - **Version:** 1.0
 - **Model Developers:** Neural Magic
 
-Quantized version of [DeepSeek-Coder-V2-Lite-Instruct](deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct).
+Quantized version of [DeepSeek-Coder-V2-Lite-Instruct](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct).
 <!-- It achieves an average score of 73.19 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 73.48. -->
 It achieves an average score of 79.60 on the [HumanEval+](https://github.com/openai/human-eval?tab=readme-ov-file) benchmark, whereas the unquantized model achieves 79.33.
 
 ### Model Optimizations
 
-This model was obtained by quantizing the weights and activations of [DeepSeek-Coder-V2-Lite-Instruct](deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) to FP8 data type, ready for inference with vLLM >= 0.5.2.
+This model was obtained by quantizing the weights and activations of [DeepSeek-Coder-V2-Lite-Instruct](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) to FP8 data type, ready for inference with vLLM >= 0.5.2.
 This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
 
 Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a linear scaling per output dimension maps the FP8 representations of the quantized weights and activations.