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+# vLLM Kimi-K2.5-Thinking Eagle3 Drafter
+
+A convenience Docker image that bundles the [Eagle3 drafter model](https://huggingface.co/nvidia/Kimi-K2.5-Thinking-Eagle3) into the vLLM container, so you can deploy speculative decoding without a separate model download step.
+
+## What's Inside
+
+- **Base image:** `vllm/vllm-openai:v0.19.0`
+- **Drafter model:** `nvidia/Kimi-K2.5-Thinking-Eagle3` (Eagle3 speculator layers) extracted to `/opt/`
+
+> **Note:** This only works with `nvidia/Kimi-K2-Thinking-NVFP4` — the text generation model. It is **not** compatible with the multimodal Kimi 2.5.
+
+## Pull
+
+```bash
+docker pull atl.vultrcr.com/vllm/vllm-kimi25-eagle:v0.19.0
+```
+
+## Usage
+
+Add the speculative decoding config to your vLLM launch args. Here's a known-working Kubernetes deployment snippet:
+
+```yaml
+- "--tensor-parallel-size=8"
+- "--trust-remote-code"
+- "--gpu-memory-utilization=0.92"
+- "--enable-auto-tool-choice"
+- "--tool-call-parser=kimi_k2"
+- "--reasoning-parser=kimi_k2"
+- "--speculative_config"
+- '{"model": "/opt/nvidia-Kimi-K2.5-Thinking-Eagle3/models--nvidia--Kimi-K2.5-Thinking-Eagle3/snapshots/13dab2a34d650a93196d37f2af91f74b8b855bab", "draft_tensor_parallel_size": 1, "num_speculative_tokens": 3, "method": "eagle3"}'
+```
+
+### Speculative Config Breakdown
+
+| Parameter | Value | Notes |
+|---|---|---|
+| `model` | `/opt/nvidia-Kimi-K2.5-Thinking-Eagle3/...` | Path to the drafter inside the container |
+| `draft_tensor_parallel_size` | `1` | TP size for the drafter |
+| `num_speculative_tokens` | `3` | Number of tokens to speculate per step |
+| `method` | `eagle3` | Speculative decoding method |
+
+## Building
+
+The Jenkins pipeline builds and pushes this image. Trigger a build with a specific tag:
+
+```bash
+curl -X POST "https://jenkins.sweetapi.com/job/vllm-kimi25-eagle/buildWithParameters" \
+  -u "$JENKINS_USER:$JENKINS_PASS" \
+  -d "TAG=v0.19.0"
+```
+
+To build locally:
+
+```bash
+docker build -t atl.vultrcr.com/vllm/vllm-kimi25-eagle:v0.19.0 .
+```