57 lines
1.9 KiB
Markdown
57 lines
1.9 KiB
Markdown
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# vLLM Kimi-K2.5-Thinking Eagle3 Drafter
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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.
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## What's Inside
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- **Base image:** `vllm/vllm-openai:v0.19.0`
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- **Drafter model:** `nvidia/Kimi-K2.5-Thinking-Eagle3` (Eagle3 speculator layers) extracted to `/opt/`
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> **Note:** This only works with `nvidia/Kimi-K2-Thinking-NVFP4` — the text generation model. It is **not** compatible with the multimodal Kimi 2.5.
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## Pull
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```bash
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docker pull atl.vultrcr.com/vllm/vllm-kimi25-eagle:v0.19.0
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```
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## Usage
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Add the speculative decoding config to your vLLM launch args. Here's a known-working Kubernetes deployment snippet:
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```yaml
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- "--tensor-parallel-size=8"
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- "--trust-remote-code"
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- "--gpu-memory-utilization=0.92"
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- "--enable-auto-tool-choice"
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- "--tool-call-parser=kimi_k2"
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- "--reasoning-parser=kimi_k2"
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- "--speculative_config"
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- '{"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"}'
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```
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### Speculative Config Breakdown
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| Parameter | Value | Notes |
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|---|---|---|
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| `model` | `/opt/nvidia-Kimi-K2.5-Thinking-Eagle3/...` | Path to the drafter inside the container |
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| `draft_tensor_parallel_size` | `1` | TP size for the drafter |
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| `num_speculative_tokens` | `3` | Number of tokens to speculate per step |
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| `method` | `eagle3` | Speculative decoding method |
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## Building
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The Jenkins pipeline builds and pushes this image. Trigger a build with a specific tag:
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```bash
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curl -X POST "https://jenkins.sweetapi.com/job/vllm-kimi25-eagle/buildWithParameters" \
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-u "$JENKINS_USER:$JENKINS_PASS" \
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-d "TAG=v0.19.0"
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```
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To build locally:
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```bash
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docker build -t atl.vultrcr.com/vllm/vllm-kimi25-eagle:v0.19.0 .
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```
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