# 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. Also includes a patched tool-call parser that fixes streaming failures caused by speculative decoding.

## 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/`
- **Patched tool parser:** `kimi_k2_tool_parser.py` — re-parse-and-diff replacement for the upstream parser

> **Note:** This only works with `nvidia/Kimi-K2-Thinking-NVFP4` — the text generation model. It is **not** compatible with the multimodal Kimi 2.5.

## The Problem: Speculative Decoding Breaks Tool Call Parsing

The upstream `kimi_k2` tool parser uses a **token-count state machine** to track streaming state — it counts how many `<|tool_call_begin|>` and `<|tool_call_end|>` tokens have arrived and uses those counts to decide whether the model is generating text or inside a tool call.

This works fine with standard autoregressive decoding, where tokens arrive one at a time. But Eagle3 speculative decoding is non-deterministic about how many tokens arrive in each streaming chunk — it can emit anywhere from 1 to `num_speculative_tokens + 1` tokens per step. When multiple structural tokens land in the same delta, the state machine breaks.

### Symptom 1: Tool calls never fire

`<|tool_calls_section_begin|>` and `<|tool_call_begin|>` arrive together in one delta. The parser checks the `<|tool_call_begin|>` count, but only `<|tool_calls_section_begin|>` has been seen so far — `cur_tool_start_count == cur_tool_end_count == 0`, so the parser thinks it's still "generating text" and forwards the section-begin token as plain content. The model says it wants to make a tool call, but the parser never enters the tool-call path.

### Symptom 2: Model goes silent after a tool call

`<|tool_call_end|>` and `<|tool_calls_section_end|>` arrive in the same delta. The same count mismatch prevents the parser from transitioning out of the tool-call state. The model completes the tool call but never resumes generating text.

### The Fix: Re-parse-and-diff

The patched parser replaces the token-count state machine with a **re-parse-and-diff** approach. On every streaming call it re-scans the entire `current_text`, finds all tool-call regions (complete and in-progress), extracts JSON arguments, and diffs against what was previously sent. Because the parser doesn't rely on counting tokens incrementally, it's correct regardless of how many tokens arrive per step — whether the speculative decoder emits 1 token or 5, the parser handles it.

This is the same approach used in the [vllm-deepseek-v32-mtp](https://sweetapi.com/biondizzle/vllm-deepseek-v32-mtp) parser for DeepSeek-V3.2, adapted for the Kimi-K2 tool call format.

## 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 .
```