Create README.md

README.md

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+---
+tags:
+- vllm
+- vision
+- fp8
+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
+---
+
+# pixtral-12b-FP8-Dynamic
+
+## Model Overview
+- **Model Architecture:** mgoin/pixtral-12b
+  - **Input:** Vision-Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** FP8
+  - **Activation quantization:** FP8
+- **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 FP8 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-FP8-Dynamic",
+    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.transformers import oneshot
+from llmcompressor.transformers.tracing import TraceableLlavaForConditionalGeneration
+from llmcompressor.modifiers.quantization import QuantizationModifier
+import os
+
+# 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)
+
+# Recipe
+recipe = [
+    QuantizationModifier(
+        targets="Linear",
+        scheme="FP8_DYNAMIC",
+        sequential_targets=["MistralDecoderLayer"],
+        ignore=["re:.*lm_head", "re:vision_tower.*", "re:multi_modal_projector.*"],
+    ),
+]
+
+SAVE_DIR=f"{model_id.split('/')[1]}-FP8-Dynamic"
+
+# Perform oneshot
+oneshot(
+    model=model,
+    recipe=recipe,
+    trust_remote_code_model=True,
+    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 neuralmagic/pixtral-12b-FP8-Dynamic --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>
+      <th rowspan="3" valign="top">A6000x1</th>
+      <th>mgoin/pixtral-12b</th>
+      <td></td>
+      <td>5.7</td>
+      <td>796</td>
+      <td>4.8</td>
+      <td>929</td>
+      <td>4.7</td>
+      <td>964</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/pixtral-12b-quantized.w8a8</th>
+      <td>1.55</td>
+      <td>3.7</td>
+      <td>1220</td>
+      <td>3.1</td>
+      <td>1437</td>
+      <td>3.0</td>
+      <td>1511</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/pixtral-12b-quantized.w4a16</th>
+      <td>2.16</td>
+      <td>3.2</td>
+      <td>1417</td>
+      <td>2.1</td>
+      <td>2093</td>
+      <td>1.9</td>
+      <td>2371</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">A100x1</th>
+      <th>mgoin/pixtral-12b</th>
+      <td></td>
+      <td>3.0</td>
+      <td>676</td>
+      <td>2.4</td>
+      <td>825</td>
+      <td>2.3</td>
+      <td>859</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/pixtral-12b-quantized.w8a8</th>
+      <td>1.38</td>
+      <td>2.2</td>
+      <td>904</td>
+      <td>1.7</td>
+      <td>1159</td>
+      <td>1.7</td>
+      <td>1201</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/pixtral-12b-quantized.w4a16</th>
+      <td>1.83</td>
+      <td>1.8</td>
+      <td>1096</td>
+      <td>1.3</td>
+      <td>1557</td>
+      <td>1.2</td>
+      <td>1702</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">H100x1</th>
+      <th>mgoin/pixtral-12b</th>
+      <td></td>
+      <td>1.8</td>
+      <td>595</td>
+      <td>1.5</td>
+      <td>732</td>
+      <td>1.4</td>
+      <td>764</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/pixtral-12b-FP8-Dynamic</th>
+      <td>1.35</td>
+      <td>1.4</td>
+      <td>767</td>
+      <td>1.1</td>
+      <td>1008</td>
+      <td>1.0</td>
+      <td>1056</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/pixtral-12b-quantized.w4a16</th>
+      <td>1.37</td>
+      <td>1.4</td>
+      <td>787</td>
+      <td>1.1</td>
+      <td>1018</td>
+      <td>1.0</td>
+      <td>1065</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>
+      <th rowspan="3" valign="top">A6000x1</th>
+      <th>mgoin/pixtral-12b</th>
+      <td></td>
+      <td>0.6</td>
+      <td>2632</td>
+      <td>0.9</td>
+      <td>4108</td>
+      <td>1.1</td>
+      <td>4774</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/pixtral-12b-quantized.w8a8</th>
+      <td>1.50</td>
+      <td>0.9</td>
+      <td>3901</td>
+      <td>1.4</td>
+      <td>6160</td>
+      <td>1.6</td>
+      <td>7292</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/pixtral-12b-quantized.w4a16</th>
+      <td>1.41</td>
+      <td>0.6</td>
+      <td>2890</td>
+      <td>1.3</td>
+      <td>5758</td>
+      <td>1.8</td>
+      <td>8312</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">A100x1</th>
+      <th>mgoin/pixtral-12b</th>
+      <td></td>
+      <td>1.1</td>
+      <td>2291</td>
+      <td>1.8</td>
+      <td>3670</td>
+      <td>2.1</td>
+      <td>4284</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/pixtral-12b-quantized.w8a8</th>
+      <td>1.38</td>
+      <td>1.5</td>
+      <td>3096</td>
+      <td>2.5</td>
+      <td>5076</td>
+      <td>3.0</td>
+      <td>5965</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/pixtral-12b-quantized.w4a16</th>
+      <td>1.40</td>
+      <td>1.4</td>
+      <td>2728</td>
+      <td>2.6</td>
+      <td>5133</td>
+      <td>3.5</td>
+      <td>6943</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">H100x1</th>
+      <th>BF16</th>
+      <td></td>
+      <td>2.6</td>
+      <td>2877</td>
+      <td>4.0</td>
+      <td>4372</td>
+      <td>4.7</td>
+      <td>5095</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/pixtral-12b-FP8-Dynamic</th>
+      <td>1.33</td>
+      <td>3.4</td>
+      <td>3753</td>
+      <td>5.4</td>
+      <td>5862</td>
+      <td>6.3</td>
+      <td>6917</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/pixtral-12b-quantized.w4a16</th>
+      <td>1.22</td>
+      <td>2.8</td>
+      <td>3115</td>
+      <td>5.0</td>
+      <td>5511</td>
+      <td>6.2</td>
+      <td>6777</td>
+    </tr>
+  </tbody>
+</table>