Create README.md

README.md

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+---
+tags:
+- w4a16
+- int4
+- vllm
+- vision
+license: apache-2.0
+license_link: https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md
+language:
+  - en
+base_model: meta-llama/Llama-3.2-11B-Vision-Instruct
+library_name: transformers
+---
+
+# 
+Llama-3.2-11B-Vision-Instruct-quantized.w4a16
+
+## Model Overview
+- **Model Architecture:** Llama-3.2-11B-Vision-Instruct
+  - **Input:** Vision-Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT4
+  - **Activation quantization:** FP16
+- **Release Date:** 1/31/2025
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
+
+Quantized version of [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct).
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) to INT4 data type, ready for inference with vLLM >= 0.5.2.
+
+## 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
+from transformers import AutoProcessor
+from vllm.assets.image import ImageAsset
+from vllm import LLM, SamplingParams
+
+# prepare model
+model_id = "neuralmagic/Llama-3.2-11B-Vision-Instruct-W4A16-G128"
+llm = LLM(
+    model=model_id,
+    max_model_len=4096,
+    max_num_seqs=16,
+    limit_mm_per_prompt={"image": 1},
+)
+processor = AutoProcessor.from_pretrained(model_id)
+
+
+# prepare inputs
+question = "What is the content of this image?"
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image"},
+            {"type": "text", "text": f"{question}"},
+        ],
+    },
+]
+prompt = processor.apply_chat_template(
+    messages, add_generation_prompt=True,tokenize=False
+)
+image = ImageAsset("cherry_blossom").pil_image.convert("RGB")
+inputs = {
+    "prompt": prompt,
+    "multi_modal_data": {
+        "image": image
+    },
+}
+
+# generate response
+print("========== SAMPLE GENERATION ==============")
+outputs = llm.generate(inputs, SamplingParams(temperature=0.2, max_tokens=64))
+print(f"PROMPT  : {outputs[0].prompt}")
+print(f"RESPONSE: {outputs[0].outputs[0].text}")
+print("==========================================")
+```
+
+vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
+
+## Creation
+
+This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below as part a multimodal announcement blog.
+
+```python
+import requests
+import torch
+from PIL import Image
+from transformers import AutoProcessor
+
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers import oneshot
+from llmcompressor.transformers.tracing import TraceableMllamaForConditionalGeneration
+
+# Load model.
+model_id = "meta-llama/Llama-3.2-11B-Vision-Instruct"
+model = TraceableMllamaForConditionalGeneration.from_pretrained(
+    model_id, device_map="auto", torch_dtype="auto"
+)
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+
+# Oneshot arguments
+DATASET_ID = "flickr30k"
+DATASET_SPLIT = {"calibration": "test[:512]"}
+NUM_CALIBRATION_SAMPLES = 512
+MAX_SEQUENCE_LENGTH = 2048
+
+
+# Define a oneshot data collator for multimodal inputs.
+def data_collator(batch):
+    assert len(batch) == 1
+    return {key: torch.tensor(value) for key, value in batch[0].items()}
+
+
+# Recipe
+recipe = [
+    GPTQModifier(
+        targets="Linear",
+        scheme="W4A16",
+        ignore=["re:.*lm_head", "re:multi_modal_projector.*", "re:vision_model.*"],
+    ),
+]
+
+# Perform oneshot
+oneshot(
+    model=model,
+    tokenizer=model_id,
+    dataset=DATASET_ID,
+    splits=DATASET_SPLIT,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    trust_remote_code_model=True,
+    data_collator=data_collator,
+)
+```
+
+## License
+
+License: Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).