Update README.md

README.md

@@ -2,9 +2,100 @@
 tags:
 - fp8
 - vllm
+language:
+- en
+- de
+- fr
+- it
+- pt
+- hi
+- es
+- th
+pipeline_tag: text-generation
+license: llama3.2
+base_model: meta-llama/Llama-3.2-90B-Vision-Instruct
 ---
 
-Run with `vllm==0.6.2` on 4xH100:
-```
+# Llama-3.2-90B-Vision-Instruct-FP8-dynamic
+
+## Model Overview
+- **Model Architecture:** Meta-Llama-3.2
+  - **Input:** Text/Image
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** FP8
+  - **Activation quantization:** FP8
+- **Intended Use Cases:** Intended for commercial and research use in multiple languages. Similarly to [Llama-3.2-90B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-90B-Vision-Instruct), this models is intended for assistant-like chat.
+- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English.
+- **Release Date:** 9/25/2024
+- **Version:** 1.0
+- **License(s):** [llama3.2](https://huggingface.co/meta-llama/Llama-3.2-90B-Vision-Instruct/blob/main/LICENSE)
+- **Model Developers:** Neural Magic
+
+Quantized version of [Llama-3.2-90B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-90B-Vision-Instruct).
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights and activations of [Llama-3.2-90B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-90B-Vision-Instruct) to FP8 data type, ready for inference with vLLM built from source.
+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-channel quantization is applied, in which a linear scaling per output dimension maps the FP8 representations of the quantized weights and activations. Activations are also quantized on a per-token dynamic basis.
+[LLM Compressor](https://github.com/vllm-project/llm-compressor) is used for quantization.
+
+## Deployment
+
+### Use with vLLM
+
+This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
+
+```python
 vllm serve neuralmagic/Llama-3.2-90B-Vision-Instruct-FP8-dynamic --enforce-eager --max-num-seqs 16 --tensor-parallel-size 4
-```
+```
+
+## Creation
+
+This model was created by applying [LLM Compressor](https://github.com/vllm-project/llm-compressor/blob/f90013702b15bd1690e4e2fe9ed434921b6a6199/examples/quantization_w8a8_fp8/llama3.2_vision_example.py), as presented in the code snipet below.
+
+```python
+from transformers import AutoProcessor, MllamaForConditionalGeneration
+
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.transformers import oneshot, wrap_hf_model_class
+
+MODEL_ID = "meta-llama/Llama-3.2-90B-Vision-Instruct"
+
+# Load model.
+model_class = wrap_hf_model_class(MllamaForConditionalGeneration)
+model = model_class.from_pretrained(MODEL_ID, device_map="auto", torch_dtype="auto")
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+
+# Configure the quantization algorithm and scheme.
+# In this case, we:
+#   * quantize the weights to fp8 with per channel via ptq
+#   * quantize the activations to fp8 with dynamic per token
+recipe = QuantizationModifier(
+    targets="Linear",
+    scheme="FP8_DYNAMIC",
+    ignore=["re:.*lm_head", "re:multi_modal_projector.*", "re:vision_model.*"],
+)
+
+# Apply quantization and save to disk in compressed-tensors format.
+SAVE_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic"
+oneshot(model=model, recipe=recipe, output_dir=SAVE_DIR)
+processor.save_pretrained(SAVE_DIR)
+
+# Confirm generations of the quantized model look sane.
+print("========== SAMPLE GENERATION ==============")
+input_ids = processor(text="Hello my name is", return_tensors="pt").input_ids.to("cuda")
+output = model.generate(input_ids, max_new_tokens=20)
+print(processor.decode(output[0]))
+print("==========================================")
+```
+
+## Evaluation
+
+TBD
+
+### Reproduction
+
+TBD