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: mgoin/pixtral-12b
+library_name: transformers
+---
+
+# Phi-3-vision-128k-instruct-W4A16-G128
+
+## Model Overview
+- **Model Architecture:** mgoin/pixtral-12b
+  - **Input:** Vision-Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT4
+  - **Activation quantization:** FP16
+- **Release Date:** 2/24/2025
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
+
+Quantized version of [mgoin/pixtral-12b](https://huggingface.co/mgoin/pixtral-12b).
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [mgoin/pixtral-12b](https://huggingface.co/mgoin/pixtral-12b) 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 vllm.assets.image import ImageAsset
+from vllm import LLM, SamplingParams
+
+# prepare model
+llm = LLM(
+    model="neuralmagic/pixtral-12b-quantized.w4a16",
+    trust_remote_code=True,
+    max_model_len=4096,
+    max_num_seqs=2,
+)
+
+# prepare inputs
+question = "What is the content of this image?"
+inputs = {
+    "prompt": f"<|user|>\n<|image_1|>\n{question}<|end|>\n<|assistant|>\n",
+    "multi_modal_data": {
+        "image": ImageAsset("cherry_blossom").pil_image.convert("RGB")
+    },
+}
+
+# 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.
+
+<details>
+  <summary>Model Creation Code</summary>
+  
+```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 TraceableLlavaForConditionalGeneration
+import os
+from clearml import InputModel, OutputModel
+from compressed_tensors.quantization import QuantizationArgs, QuantizationType, QuantizationStrategy, ActivationOrdering, QuantizationScheme
+
+# Load model.
+model_id = "mgoin/pixtral-12b"
+model = TraceableLlavaForConditionalGeneration.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
+dampening_frac=0.01
+
+# Define a oneshot data collator for multimodal inputs.
+def data_collator(batch):
+    assert len(batch) == 1
+    return {
+        "input_ids": torch.LongTensor(batch[0]["input_ids"]),
+        "attention_mask": torch.tensor(batch[0]["attention_mask"]),
+        "pixel_values": torch.tensor(batch[0]["pixel_values"]),
+    }
+
+recipe = GPTQModifier(
+    targets="Linear",
+    config_groups={
+        "config_group": QuantizationScheme(
+            targets=["Linear"],
+            weights=QuantizationArgs(
+                num_bits=4,
+                type=QuantizationType.INT,
+                strategy=QuantizationStrategy.GROUP,
+                group_size=128,
+                symmetric=True,
+                dynamic=False,
+                actorder=ActivationOrdering.WEIGHT,
+            ),
+        ),
+    },
+    sequential_targets=["MistralDecoderLayer"],
+    ignore=["re:.*lm_head", "re:vision_tower.*", "re:multi_modal_projector.*"],
+    update_size=NUM_CALIBRATION_SAMPLES,
+    dampening_frac=dampening_frac,
+)
+
+SAVE_DIR = f"{model_id.split('/')[1]}-quantized.w4a16"
+
+# 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,
+    output_dir=SAVE_DIR
+)
+
+```
+</details>
+
+## Evaluation
+
+The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard), OpenLLM Leaderboard [V2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/) and on [HumanEval](https://github.com/neuralmagic/evalplus), using the following commands:
+
+<details>
+<summary>Evaluation Commands</summary>
+
+```
+guidellm --model neuralmagic/granite-3.1-8b-instruct-quantized.w4a16 --target "http://localhost:8000/v1" --data-type emulated --data "prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>" --max seconds 360 --backend aiohttp_server
+```
+
+</details>
+
+### Accuracy
+
+## Inference Performance
+
+
+This model achieves up to xxx speedup in single-stream deployment and up to xxx speedup in multi-stream asynchronous deployment, depending on hardware and use-case scenario.
+The following performance benchmarks were conducted with [vLLM](https://docs.vllm.ai/en/latest/) version 0.7.2, and [GuideLLM](https://github.com/neuralmagic/guidellm).
+
+<details>
+<summary>Benchmarking Command</summary>
+
+</details>
+
+### Single-stream performance (measured with vLLM version 0.7.2)
+
+### Multi-stream asynchronous performance (measured with vLLM version 0.7.2)