Create README.md

README.md

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+---
+tags:
+- w8a8
+- int8
+- 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: nm-testing/Pixtral-Large-Instruct-2411-hf
+library_name: transformers
+---
+
+# Pixtral-Large-Instruct-2411-hf-quantized.w8a8
+
+## Model Overview
+- **Model Architecture:** nm-testing/Pixtral-Large-Instruct-2411-hf
+  - **Input:** Vision-Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT8
+  - **Activation quantization:** INT8
+- **Release Date:** 2/24/2025
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
+
+Quantized version of [nm-testing/Pixtral-Large-Instruct-2411-hf](https://huggingface.co/nm-testing/Pixtral-Large-Instruct-2411-hf/tree/main).
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [nm-testing/Pixtral-Large-Instruct-2411-hf](https://huggingface.co/nm-testing/Pixtral-Large-Instruct-2411-hf/tree/main) to INT8 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-Large-Instruct-2411-hf-quantized.w8a8",
+    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
+        
+# Load model.
+model_id = "nm-testing/Pixtral-Large-Instruct-2411-hf"
+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
+
+
+# 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
+recipe = [
+    GPTQModifier(
+        targets="Linear",
+        scheme="W8A8",
+        sequential_targets=["MistralDecoderLayer"],
+        ignore=["re:.*lm_head", "re:vision_tower.*", "re:multi_modal_projector.*"],
+    ),
+]
+
+SAVE_DIR==f"{model_id.split('/')[1]}-quantized.w8a8"
+
+# 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>
+
+```
+```
+
+</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>
+```
+  guidellm --model nm-testing/Pixtral-Large-Instruct-2411-hf-quantized.w8a8 --target "http://localhost:8000/v1" --data-type emulated --data prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>,images=<num_images>,width=<image_width>,height=<image_height> --max seconds 120 --backend aiohttp_server
+```
+
+</details>
+
+### Single-stream performance (measured with vLLM version 0.7.2)
+
+<table border="1" class="dataframe">
+  <thead>
+    <tr>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th style="text-align: center;" colspan="2" >Document Visual Question Answering<br>1680W x 2240H<br>64/128</th>
+      <th style="text-align: center;" colspan="2" >Visual Reasoning <br>640W x 480H<br>128/128</th>
+      <th style="text-align: center;" colspan="2" >Image Captioning<br>480W x 360H<br>0/128</th>
+    </tr>
+    <tr>
+      <th>Hardware</th>
+      <th>Model</th>
+      <th>Average Cost Reduction</th>
+      <th>Latency (s)</th>
+      <th>QPD</th>
+      <th>Latency (s)th>
+      <th>QPD</th>
+      <th>Latency (s)</th>
+      <th>QPD</th>
+    </tr>
+  </thead>
+  <tbody style="text-align: center">
+    <tr>
+      <td>A100x4</td>
+      <td>nm-testing/Pixtral-Large-Instruct-2411-hf</td>
+      <td></td>
+      <td>7.5</td>
+      <td>67</td>
+      <td>6.5</td>
+      <td>77</td>
+      <td>6.4</td>
+      <td>79</td>
+    </tr>
+    <tr>
+      <td>A100x2</td>
+      <td>nm-testing/Pixtral-Large-Instruct-2411-hf-quantized.w8a8</td>
+      <td>1.86</td>
+      <td>8.1</td>
+      <td>124</td>
+      <td>7.1</td>
+      <td>142</td>
+      <td>6.8</td>
+      <td>148</td>
+    </tr>
+    <tr>
+      <td>A100x2</td>
+      <td>nm-testing/Pixtral-Large-Instruct-2411-hf-quantized.w4a16</td>
+      <td>2.52</td>
+      <td>6.9</td>
+      <td>147</td>
+      <td>5.1</td>
+      <td>199</td>
+      <td>4.5</td>
+      <td>221</td>
+    </tr>
+    <tr>
+      <td>H100x4</td>
+      <td>nm-testing/Pixtral-Large-Instruct-2411-hf</td>
+      <td></td>
+      <td>4.4</td>
+      <td>67</td>
+      <td>3.9</td>
+      <td>74</td>
+      <td>3.7</td>
+      <td>79</td>
+    </tr>
+    <tr>
+      <td>H100x2</td>
+      <td>nm-testing/Pixtral-Large-Instruct-2411-hf-FP8-Dynamic</td>
+      <td>1.82</td>
+      <td>4.7</td>
+      <td>120</td>
+      <td>4.1</td>
+      <td>137</td>
+      <td>3.9</td>
+      <td>145</td>
+    </tr>
+    <tr>
+      <td>H100x2</td>
+      <td>nm-testing/Pixtral-Large-Instruct-2411-hf-quantized.w4a16</td>
+      <td>1.87</td>
+      <td>4.7</td>
+      <td>120</td>
+      <td>3.9</td>
+      <td>144</td>
+      <td>3.8</td>
+      <td>149</td>
+    </tr>
+  </tbody>
+</table>
+ 
+
+
+### Multi-stream asynchronous performance (measured with vLLM version 0.7.2)
+
+<table border="1" class="dataframe">
+  <thead>
+    <tr>
+      <th></th>
+      <th></th>
+      <th></th>
+      <th style="text-align: center;" colspan="2" >Document Visual Question Answering<br>1680W x 2240H<br>64/128</th>
+      <th style="text-align: center;" colspan="2" >Visual Reasoning <br>640W x 480H<br>128/128</th>
+      <th style="text-align: center;" colspan="2" >Image Captioning<br>480W x 360H<br>0/128</th>
+    </tr>
+    <tr>
+      <th>Hardware</th>
+      <th>Model</th>
+      <th>Average Cost Reduction</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>QPD</th>
+    </tr>
+  </thead>
+  <tbody style="text-align: center">
+   <tr>
+      <td>A100x4</td>
+      <td>nm-testing/Pixtral-Large-Instruct-2411-hf</td>
+      <td></td>
+      <td>0.4</td>
+      <td>222</td>
+      <td>0.7</td>
+      <td>341</td>
+      <td>0.8</td>
+      <td>399</td>
+    </tr>
+    <tr>
+      <td>A100x2</td>
+      <td>nm-testing/Pixtral-Large-Instruct-2411-hf-quantized.w8a8</td>
+      <td>1.70</td>
+      <td>0.8</td>
+      <td>383</td>
+      <td>1.1</td>
+      <td>571</td>
+      <td>1.3</td>
+      <td>674</td>
+    </tr>
+    <tr>
+      <td>A100x2</td>
+      <td>nm-testing/Pixtral-Large-Instruct-2411-hf-quantized.w4a16</td>
+      <td>1.48</td>
+      <td>0.5</td>
+      <td>276</td>
+      <td>1.0</td>
+      <td>505</td>
+      <td>1.4</td>
+      <td>680</td>
+    </tr>
+    <tr>
+      <td>H100x4</td>
+      <td>nm-testing/Pixtral-Large-Instruct-2411-hf</td>
+      <td></td>
+      <td>1.0</td>
+      <td>284</td>
+      <td>1.6</td>
+      <td>465</td>
+      <td>1.8</td>
+      <td>511</td>
+    </tr>
+    <tr>
+      <td>H100x2</td>
+      <td>nm-testing/Pixtral-Large-Instruct-2411-hf-FP8-Dynamic</td>
+      <td>1.61</td>
+      <td>1.7</td>
+      <td>467</td>
+      <td>2.6</td>
+      <td>726</td>
+      <td>3.2</td>
+      <td>908</td>
+    </tr>
+    <tr>
+      <td>H100x2</td>
+      <td>nm-testing/Pixtral-Large-Instruct-2411-hf-quantized.w4a16</td>
+      <td>1.33</td>
+      <td>1.4</td>
+      <td>393</td>
+      <td>2.2</td>
+      <td>634</td>
+      <td>2.7</td>
+      <td>764</td>
+    </tr>
+  </tbody>
+</table>