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README.md

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+# Keye-VL-671B-A37B
+
+
+Meet Keye-VL-671B-A37B — the most powerful multi-modal language model in the Keye series to date.
+
+As one of the largest and most capable MLLMs currently in existence, Keye-VL 671B demonstrates achieved top-tier and in some cases even leading performance in text understanding and generation, complex visual perception and reasoning, comprehensive video understanding, and Olympic-level mathematical reasoning.
+
+#### Key Enhancements:
+
+##### Pre-Training
+* **Efficient Perception Building with Limited Compute**: We employ VisionEncoder from Keye-VL-1.5 and rigorously processed high-quality data to cost-effectively build the model’s core perceptual capabilities, ensuring strong visual understanding without excessive computational overhead.
+
+* **Multi-Modal Data Curation**: We implement a automated data pipeline that performs strict filtering, re-sampling, and large-scale synthesis of structured VQA data, including OCR, charts, and tables. This end-to-end process significantly enhances the model’s perception quality and generalization.
+
+* **Reasoning Sustainment via Synthetic CoT Data**: During the continual pretrain phase, we incorporate a diverse set of synthetically generated chain-of-thought (CoT) data. This ensures the model maintains its complex reasoning skills while progressing in perceptual pre-training.
+
+##### Post-Training
+
+* **Scaling law of Reasoning Data for SFT**: We experimentally validate that the mixed data (50B Instruct & Long-CoT data) improves model performance and training stability compared to the single model (30B Instruct data).
+* **CoT Quality & Style Refinement**: We develop a data filtering process to remove redundant reflective chains, improving the model's reasoning and perception capabilities, with the in-house process outperforming GPT-4o.
+* **High-Precision RL Verifier**: We train a dedicated verifier (Keye Verifier) to validate the model's reasoning  consistency and answer correctness, achieving significantly higher accuracy than other reward models and general LLMs, thereby enhancing our RL performance gains.
+
+## Model Performance
+
+![Performance Comparison](figures/radar.png)
+
+|                       | Benchmarks     | Seed1.5-VL thinking | dots.vlm1 | Qwen3-VL-235B-A22B thinking | Keye-VL-1.5-671B-A37B |
+| --------------------- | -------------- | :-----------------: | :-------: | :-------------------------: | :-------------------: |
+| STEM/Reasoning        | MMMU_VAL       |        77.9         |   80.11   |            80.6             |       **83.78**       |
+|                       | MMMU_Pro       |        67.6         |   70.11   |            69.3             |       **72.49**       |
+|                       | MathVision     |        68.7         |   69.64   |          **74.6**           |         69.11         |
+|                       | MathVista      |        85.6         |   85.0    |            85.8             |       **86.2**        |
+|                       | OlympiadBench  |        65.0         |     -     |              -              |       **74.92**       |
+|                       | VisuLogic      |        35.0         |   32.2    |            34.4             |       **35.4**        |
+| General VQA           | RealWorldQA    |        78.4         |   79.08   |            81.3             |       **86.54**       |
+|                       | MMStar         |        77.8         |   76.67   |            78.7             |       **86.67**       |
+|                       | MMBench-en     |        89.9         |   89.32   |            90.6             |       **95.74**       |
+|                       | MMbench-cn     |        89.1         |   88.24   |              -              |       **94.27**       |
+|                       | MMVP           |        69.3         |   72.0    |              -              |       **88.0**        |
+|                       | V*             |        89.0         |     -     |              -              |       **90.05**       |
+|                       | HallusionBench |        60.3         |   64.83   |            66.7             |       **72.3**        |
+| Video                 | VideoMME       |        77.9         |     -     |          **79.0**           |       **79.0**        |
+|                       | LongVideoBench |        74.0         |     -     |         65.2 (fp8)          |       **79.0**        |
+|                       | MMVU           |        70.1         |     -     |         78.4 (fp8)          |       **86.6**        |
+|                       | TempCompass    |      **83.7**       |     -     |         81.03 (fp8)         |         77.75         |
+| Text Recog./Doc/chart | TextVQA        |      **81.8**       |     -     |              -              |         76.21         |
+|                       | DocVQA_VAL     |      **96.9**       |   96.52   |            96.5             |         95.39         |
+|                       | ChartQA_TEST   |      **89.1**       |   87.68   |              -              |         86.68         |
+|                       | InfoVQA        |      **91.2**       |     -     |            89.5             |         86.93         |
+|                       | CharXiv (RQ)   |        60.2         |   64.4    |            66.1             |       **79.4**        |
+|                       | CharXiv (DQ)   |        92.6         |   92.1    |              -              |       **94.5**        |
+|                       | AI2D_TEST      |        87.3         |   88.37   |            89.2             |       **91.19**       |
+| Pure Text             | AIME2025       |          -          |   85.83   |          **89.7**           |         83.3          |
+|                       | GPQA           |          -          | **72.78** |              -              |         71.21         |
+
+## Quickstart
+
+### Environment Setup
+
+```shell
+docker run -it --gpus all lmsysorg/sglang:v0.5.2
+# make sure each node use the following commands to install the custom SGLang branch
+git clone -b keye-dpsk-infer-fp8-release https://github.com/Kwai-Keye/sglang.git sglang
+pip install -e sglang/python[all]
+```
+
+### Two-Node H800x8 Deployment
+
+#### Prerequisites:
+
+- Model: Kwai-Keye/Keye-VL-671B-A37B
+- Node 1 IP: 192.168.1.100 (MASTER_NODE_IP)
+- Node 2 IP: 192.168.1.101 (WORKER_NODE_IP)
+
+#### Node 1 (Master - rank 0):
+
+```shell
+MODEL_PATH=/path/to/Keye-VL-671B-A37B
+DIST_INIT_ADDR="MASTER_NODE_IP:29500"   # e.g. 192.168.1.100:29500
+PORT=30000                              # listening port on each node
+python3 -m sglang.launch_server \
+    --model-path $MODEL_PATH \
+    --host 0.0.0.0 \
+    --port $PORT \
+    --tp-size 16 \
+    --nnodes 2 \
+    --node-rank 0 \
+    --dist-init-addr $DIST_INIT_ADDR \
+    --trust-remote-code \
+    --mm-attention-backend fa3 \
+    --attention-backend fa3 \
+    --disable-radix-cache \
+    --mem-fraction-static 0.8 \
+    --cuda-graph-max-bs 64 \
+    --model-loader-extra-config '{"enable_multithread_load": true, "num_threads": 32}'
+```
+
+#### Node 2 (Worker - rank 1):
+
+```shell
+MODEL_PATH=/path/to/Keye-VL-671B-A37B
+DIST_INIT_ADDR="MASTER_NODE_IP:29500"   # e.g. 192.168.1.100:29500
+PORT=30000                              # listening port on each node
+python3 -m sglang.launch_server \
+    --model-path $MODEL_PATH \
+    --host 0.0.0.0 \
+    --port $PORT \
+    --tp-size 16 \
+    --nnodes 2 \
+    --node-rank 1 \
+    --dist-init-addr $DIST_INIT_ADDR \
+    --trust-remote-code \
+    --mm-attention-backend fa3 \
+    --attention-backend fa3 \
+    --disable-radix-cache \
+    --mem-fraction-static 0.8 \
+    --cuda-graph-max-bs 64 \
+    --model-loader-extra-config '{"enable_multithread_load": true, "num_threads": 32}'
+```
+
+For more deployment details, please refer to the [Keye-VL-671B-A37B Deployment Tutorial](https://github.com/Kwai-Keye/sglang/blob/keye-dpsk-infer-fp8-release/scripts/deploy_keye_deepseek/DEPLOY_TUTORIAL.md).
+
+### Client Usage
+
+```python
+import json
+import requests
+
+BASE_URL = "http://MASTER_NODE_IP:30000"
+
+def generate(messages):
+    payload = {
+        "model": "",
+        "messages": messages,
+        "n": 1,
+        "temperature": 0.0,
+        "max_tokens": 256,
+        "top_k": 1,
+        "ignore_eos": False,
+        "skip_special_tokens": True,
+    }
+    resp = requests.post(
+        f"{BASE_URL}/v1/chat/completions",
+        headers={"Content-Type": "application/json"},
+        data=json.dumps(payload),
+        timeout=1800,
+    )
+    resp.raise_for_status()
+    return resp.json()
+
+# Example: image + text
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image_url",
+                "image_url": {"url": "https://raw.githubusercontent.com/sgl-project/sglang/main/assets/logo.png"},
+            },
+            {"type": "text", "text": "Describe this image in detail."},
+        ],
+    }
+]
+
+result = generate(messages)
+print(result["choices"][0]["message"]["content"])
+```
+