Update README.md

README.md

@@ -1,3 +1,48 @@
----
-license: llama3.2
----
+---
+base_model:
+- meta-llama/Llama-3.2-90B-Vision-Instruct
+license: llama3.2
+---
+# Llama-3.2-90B-Vision-Instruct-FP8-KV
+- ## Introduction
+  This model was created by applying [Quark](https://quark.docs.amd.com/latest/index.html) with calibration samples from Pile dataset.
+- ## Quantization Stragegy
+  - ***Weight***: FP8 symmetric per-tensor
+  - ***Activation***: FP8 symmetric per-tensor
+  - ***KV Cache***: FP8 symmetric  per-tensor
+  - ***Note***: The Llama-3.2-90B-Vision-Instruct consists of two parts: the language model (MllamaForCausalLM) and the vision model (MllamaVisionModel). Here, we only quantize the MllamaForCausalLM.
+- ## Quick Start
+1. [Download and install Quark](https://quark.docs.amd.com/latest/install.html)
+2. Run the quantization script in the example folder using the following command line:
+```sh
+export MODEL_DIR = [local model checkpoint folder] or meta-llama/Llama-3.2-90B-Vision-Instruct
+# single GPU
+python3 quantize_quark.py \ 
+        --model_dir $MODEL_DIR \
+        --output_dir Llama-3.2-90B-Vision-Instruct-FP8-KV \                           
+        --quant_scheme w_fp8_a_fp8 \
+        --kv_cache_dtype fp8 \
+        --num_calib_data 128 \
+# If model size is too large for single GPU, please use multi GPU instead.
+python3 quantize_quark.py \ 
+        --model_dir $MODEL_DIR \
+        --output_dir Llama-3.2-90B-Vision-Instruct-FP8-KV \                           
+        --quant_scheme w_fp8_a_fp8 \
+        --kv_cache_dtype fp8 \
+        --num_calib_data 128 \
+  <tr>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong>Llama-3.2-90B-Vision-Instruct </strong>
+   </td>
+   <td><strong>Llama-3.2-90B-Vision-Instruct-FP8-KV(this model)</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>Perplexity-wikitext2
+   </td>
+   <td>3.7805
+   </td>
+   <td>3.8570
+   </td>
+  </tr>