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Model: RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic Source: Original Platform
2026-10-22 10:23:14 +08:00
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--- a/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: Qwen/Qwen2.5-VL-3B-Instruct
+library_name: transformers
+---
+
+# Qwen2.5-VL-3B-Instruct-FP8-Dynamic
+
+## Model Overview
+- **Model Architecture:** Qwen2.5-VL-3B-Instruct
+  - **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 [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct).
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) 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/Qwen2.5-VL-3B-Instruct-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 (
+    TraceableQwen2_5_VLForConditionalGeneration,
+)
+from llmcompressor.modifiers.quantization import QuantizationModifier
+
+# Load model.
+model_id = Qwen/Qwen2.5-VL-3B-Instruct
+model = TraceableQwen2_5_VLForConditionalGeneration.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 using [mistral-evals](https://github.com/neuralmagic/mistral-evals) for vision-related tasks and using [lm_evaluation_harness](https://github.com/neuralmagic/lm-evaluation-harness) for select text-based benchmarks. The evaluations were conducted using the following commands:
+
+<details>
+<summary>Evaluation Commands</summary>
+  
+### Vision Tasks
+- vqav2
+- docvqa
+- mathvista
+- mmmu
+- chartqa
+
+```
+vllm serve neuralmagic/pixtral-12b-quantized.w8a8 --tensor_parallel_size 1 --max_model_len 25000 --trust_remote_code --max_num_seqs 8 --gpu_memory_utilization 0.9 --dtype float16 --limit_mm_per_prompt image=7
+
+python -m eval.run eval_vllm \
+        --model_name neuralmagic/pixtral-12b-quantized.w8a8 \
+        --url http://0.0.0.0:8000 \
+        --output_dir ~/tmp \
+        --eval_name <vision_task_name>
+```
+
+### Text-based Tasks
+#### MMLU
+  
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="<model_name>",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=<n>,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
+  --tasks mmlu \
+  --num_fewshot 5 \
+  --batch_size auto \
+  --output_path output_dir
+
+```
+
+#### MGSM
+
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="<model_name>",dtype=auto,max_model_len=4096,max_gen_toks=2048,max_num_seqs=128,tensor_parallel_size=<n>,gpu_memory_utilization=0.9 \
+  --tasks mgsm_cot_native \
+  --apply_chat_template \
+  --num_fewshot 0 \
+  --batch_size auto \
+  --output_path output_dir
+
+```
+</details>
+
+
+### Accuracy
+<table>
+  <thead>
+    <tr>
+      <th>Category</th>
+      <th>Metric</th>
+      <th>Qwen/Qwen2.5-VL-3B-Instruct</th>
+      <th>nm-testing/Qwen2.5-VL-3B-Instruct-FP8-Dynamic</th>
+      <th>Recovery (%)</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td rowspan="6"><b>Vision</b></td>
+      <td>MMMU (val, CoT)<br><i>explicit_prompt_relaxed_correctness</i></td>
+      <td>44.56</td>
+      <td>45.78</td>
+      <td>102.74%</td>
+    </tr>
+    <tr>
+      <td>VQAv2 (val)<br><i>vqa_match</i></td>
+      <td>75.94</td>
+      <td>76.22</td>
+      <td>100.37%</td>
+    </tr>
+    <tr>
+      <td>DocVQA (val)<br><i>anls</i></td>
+      <td>92.53</td>
+      <td>92.40</td>
+      <td>99.86%</td>
+    </tr>
+    <tr>
+      <td>ChartQA (test, CoT)<br><i>anywhere_in_answer_relaxed_correctness</i></td>
+      <td>81.20</td>
+      <td>80.72</td>
+      <td>99.41%</td>
+    </tr>
+    <tr>
+      <td>Mathvista (testmini, CoT)<br><i>explicit_prompt_relaxed_correctness</i></td>
+      <td>54.15</td>
+      <td>53.25</td>
+      <td>98.34%</td>
+    </tr>
+    <tr>
+      <td><b>Average Score</b></td>
+      <td><b>69.28</b></td>
+      <td><b>69.67</b></td>
+      <td><b>100.56%</b></td>
+    </tr>
+    <tr>
+      <td rowspan="2"><b>Text</b></td>
+      <td>MGSM (CoT)</td>
+      <td>43.69</td>
+      <td>43.14</td>
+      <td>98.74%</td>
+    </tr>
+    <tr>
+      <td>MMLU (5-shot)</td>
+      <td>65.32</td>
+      <td>65.03</td>
+      <td>99.56%</td>
+    </tr>
+  </tbody>
+</table>
+
+
+## Inference Performance
+
+
+This model achieves up to 1.10x speedup in single-stream deployment and up to 1.32x 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/Qwen2.5-VL-3B-Instruct-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>Queries Per Dollar</th>
+      <th>Latency (s)<th>
+      <th>Queries Per Dollar</th>
+      <th>Latency (s)</th>
+      <th>Queries Per Dollar</th>
+    </tr>
+  </thead>
+  <tbody style="text-align: center">
+    <tr>
+      <th rowspan="3" valign="top">A6000x1</th>
+      <th>Qwen/Qwen2.5-VL-3B-Instruct</th>
+      <td></td>
+      <td>3.1</td>
+      <td>1454</td>
+      <td>1.8</td>
+      <td>2546</td>
+      <td>1.7</td>
+      <td>2610</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8</th>
+      <td>1.27</td>
+      <td>2.6</td>
+      <td>1708</td>
+      <td>1.3</td>
+      <td>3340</td>
+      <td>1.3</td>
+      <td>3459</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
+      <td>1.57</td>
+      <td>2.4</td>
+      <td>1886</td>
+      <td>1.0</td>
+      <td>4409</td>
+      <td>1.0</td>
+      <td>4409</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">A100x1</th>
+      <th>Qwen/Qwen2.5-VL-3B-Instruct</th>
+      <td></td>
+      <td>2.2</td>
+      <td>920</td>
+      <td>1.3</td>
+      <td>1603</td>
+      <td>1.2</td>
+      <td>1636</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8</th>
+      <td>1.09</td>
+      <td>2.1</td>
+      <td>975</td>
+      <td>1.2</td>
+      <td>1743</td>
+      <td>1.1</td>
+      <td>1814</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
+      <td>1.20</td>
+      <td>2.0</td>
+      <td>1011</td>
+      <td>1.0</td>
+      <td>2015</td>
+      <td>1.0</td>
+      <td>2012</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">H100x1</th>
+      <th>Qwen/Qwen2.5-VL-3B-Instruct</th>
+      <td>1.5</td>
+      <td>740</td>
+      <td>0.9</td>
+      <td>1221</td>
+      <td>0.9</td>
+      <td>1276</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/Qwen2.5-VL-3B-Instruct-FP8-Dynamic</th>
+      <td>1.06</td>
+      <td>1.4</td>
+      <td>768</td>
+      <td>0.9</td>
+      <td>1276</td>
+      <td>0.8</td>
+      <td>1399</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
+      <td>1.24</td>
+      <td>0.9</td>
+      <td>1219</td>
+      <td>0.9</td>
+      <td>1270</td>
+      <td>0.8</td>
+      <td>1304</td>
+    </tr>
+  </tbody>
+</table>
+ 
+**Use case profiles: Image Size (WxH) / prompt tokens / generation tokens
+
+**QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025).
+
+### 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>Queries Per Dollarv</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>Queries Per Dollar</th>
+      <th>Maximum throughput (QPS)</th>
+      <th>Queries Per Dollar</th>
+    </tr>
+  </thead>
+  <tbody style="text-align: center">
+    <tr>
+      <th rowspan="3" valign="top">A6000x1</th>
+      <th>Qwen/Qwen2.5-VL-3B-Instruct</th>
+      <td></td>
+      <td>0.5</td>
+      <td>2405</td>
+      <td>2.6</td>
+      <td>11889</td>
+      <td>2.9</td>
+      <td>12909</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8</th>
+      <td>1.26</td>
+      <td>0.6</td>
+      <td>2725</td>
+      <td>3.4</td>
+      <td>15162</td>
+      <td>3.9</td>
+      <td>17673</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
+      <td>1.39</td>
+      <td>0.6</td>
+      <td>2548</td>
+      <td>3.9</td>
+      <td>17437</td>
+      <td>4.7</td>
+      <td>21223</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">A100x1</th>
+      <th>Qwen/Qwen2.5-VL-3B-Instruct</th>
+      <td></td>
+      <td>0.8</td>
+      <td>1663</td>
+      <td>3.9</td>
+      <td>7899</td>
+      <td>4.4</td>
+      <td>8924</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8</th>
+      <td>1.06</td>
+      <td>0.9</td>
+      <td>1734</td>
+      <td>4.2</td>
+      <td>8488</td>
+      <td>4.7</td>
+      <td>9548</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
+      <td>1.10</td>
+      <td>0.9</td>
+      <td>1775</td>
+      <td>4.2</td>
+      <td>8540</td>
+      <td>5.1</td>
+      <td>10318</td>
+    </tr>
+    <tr>
+      <th rowspan="3" valign="top">H100x1</th>
+      <th>Qwen/Qwen2.5-VL-3B-Instruct</th>
+      <td></td>
+      <td>1.1</td>
+      <td>1188</td>
+      <td>4.3</td>
+      <td>4656</td>
+      <td>4.3</td>
+      <td>4676</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/Qwen2.5-VL-3B-Instruct-FP8-Dynamic</th>
+      <td>1.15</td>
+      <td>1.4</td>
+      <td>1570</td>
+      <td>4.3</td>
+      <td>4676</td>
+      <td>4.8</td>
+      <td>5220</td>
+    </tr>
+    <tr>
+      <th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
+      <td>1.96</td>
+      <td>4.2</td>
+      <td>4598</td>
+      <td>4.1</td>
+      <td>4505</td>
+      <td>4.4</td>
+      <td>4838</td>
+    </tr>
+  </tbody>
+</table>
+
+**Use case profiles: Image Size (WxH) / prompt tokens / generation tokens
+
+**QPS: Queries per second.
+
+**QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025).
--- a/added_tokens.json
+++ b/added_tokens.json
@@ -0,0 +1,24 @@
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+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}
--- a/chat_template.json
+++ b/chat_template.json
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+oid sha256:94174d7176c52a7192f96fc34eb2cf23c7c2059d63cdbfadca1586ba89731fb7
+size 1049
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--- a/configuration.json
+++ b/configuration.json
@@ -0,0 +1 @@
+{"framework": "pytorch", "task": "image-to-text", "allow_remote": true}
--- a/generation_config.json
+++ b/generation_config.json
@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
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+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.1,
+  "top_k": 1,
+  "top_p": 0.001,
+  "transformers_version": "4.49.0.dev0"
+}
--- a/merges.txt
+++ b/merges.txt
--- a/model-00001-of-00002.safetensors
+++ b/model-00001-of-00002.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d498f08b42d64e9bcedb2e2a3b5ce2e1d523fc0dfae56c72c6c8d60cd5c07706
+size 4736900104
--- a/model-00002-of-00002.safetensors
+++ b/model-00002-of-00002.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f939d272ee1b8b2b48b26ee2f8aca7b320f91d442323b5548c53efe900ec0d18
+size 622329984
--- a/model.safetensors.index.json
+++ b/model.safetensors.index.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bdf73c2a8f80a7527108d1f8368000f13fff9e89918ae94e3ee80340e93a6b56
+size 87468
--- a/preprocessor_config.json
+++ b/preprocessor_config.json
@@ -0,0 +1,29 @@
+{
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "Qwen2VLImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "max_pixels": 12845056,
+  "merge_size": 2,
+  "min_pixels": 3136,
+  "patch_size": 14,
+  "processor_class": "Qwen2_5_VLProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "longest_edge": 12845056,
+    "shortest_edge": 3136
+  },
+  "temporal_patch_size": 2
+}
--- a/recipe.yaml
+++ b/recipe.yaml
@@ -0,0 +1,6 @@
+DEFAULT_stage:
+  DEFAULT_modifiers:
+    QuantizationModifier:
+      ignore: ['re:.*lm_head', 're:visual.*']
+      targets: [Linear]
+      scheme: FP8_DYNAMIC
--- a/special_tokens_map.json
+++ b/special_tokens_map.json
@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
--- a/tokenizer.json
+++ b/tokenizer.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
+size 11421896
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c182cc5f590489eba7c5435864b0814fcd9256ef72933f8ccc142116974dbb01
+size 7300
--- a/vocab.json
+++ b/vocab.json
				`@@ -0,0 +1 @@`
				`{"framework": "pytorch", "task": "image-to-text", "allow_remote": true}`