The Quick Probe: Launching Phase 0 of EAGLE-3 Fine-Tuning

In the long arc of optimizing large language model inference, there comes a moment when analysis must yield to action. Message 4943 represents precisely that inflection point in an extended session dedicated to improving the throughput of Kimi-K2.5, a 1-trillion-parameter Mixture-of-Experts model running on eight NVIDIA RTX PRO 6000 Blackwell GPUs. After dozens of messages spent diagnosing bottlenecks, profiling verify steps, analyzing NCCL communication patterns, and authoring a comprehensive game plan, the assistant finally executes the first concrete action of Phase 0: a quick probe to determine whether a pre-trained EAGLE-3 draft model from a related architecture can be repurposed for the target model.

Context and Motivation

To understand why this message matters, one must appreciate the predicament that preceded it. The team had successfully trained an EAGLE-3 speculative decoding drafter from scratch on 37,000 samples of Kimi-K2.5 hidden state data. Despite reaching 74.7% validation accuracy, the drafter delivered only 60 tokens per second — a 27% regression compared to the baseline of 82 tok/s without speculation. The bottleneck was not the draft model itself but the "verify step," where the target model must validate each draft token. This verify step consumed approximately 30 milliseconds per cycle, accounting for 97% of the total inference time. The culprit was the PCIe interconnect between the eight GPUs: each verify pass required 122 NCCL all-reduce operations totaling roughly 25 milliseconds of pure communication overhead.

The game plan, documented in eagle-k2finetune-game-plan.md, proposed a multi-phase strategy. The highest-leverage path was to fine-tune an existing high-quality drafter rather than training from scratch. The AQ-MedAI team had released a K2 EAGLE-3 drafter trained on 1.4 million samples, achieving an acceptance length of 3.2–3.5 tokens on the Kimi-K2 model. Since K2 and K2.5 share the same DeepSeek V3 / MLA architecture — same hidden size of 7168, same intermediate size of 18432, same 61-layer MoE structure — the drafter's weights should, in theory, transfer well. The critical unknown was whether the hidden state representations at layers 2, 30, and 58 were sufficiently similar between the two model versions.

Phase 0 was designed as the cheapest possible experiment: simply plug the AQ-MedAI drafter into a K2.5 server and measure the acceptance length. If the drafter achieved an acceptance length of 2.5 or higher, it would already outperform the from-scratch model and potentially cross the break-even threshold of 2.46 needed to beat the baseline. If the acceptance length was near zero, it would indicate a fundamental representation mismatch, saving days of wasted fine-tuning effort.

The Message Itself: Three Parallel Commands

The message opens with a clear declaration of intent: "Starting Phase 0. First, let me kill any existing server processes and check the AQ-MedAI drafter directory." This sentence accomplishes two things simultaneously. It signals to the user that the game plan is being executed as agreed, and it establishes the logical sequence of operations: cleanup before setup.

The assistant then dispatches three bash commands in parallel, a pattern characteristic of the tool-calling architecture used throughout this session. All three commands run simultaneously, and the assistant will receive their outputs together before proceeding to the next round. This parallelism is not just an implementation detail — it reflects a deliberate strategy of batching independent operations to minimize wall-clock time.

Command 1: Process Cleanup

ssh root@10.1.2.6 'pct exec 129 -- bash -c "ps aux | grep python3 | grep -v grep | awk \"{print \\\$2}\" | xargs -r kill -9"' 2>&1; echo "EXIT: $?"

This command targets the Proxmox host at 10.1.2.6, which manages the LXC container (ID 129, hostname llm-two) where all inference and training runs. The command chain is worth unpacking: it lists all running Python processes, filters out the grep command itself, extracts process IDs, and kills them with SIGKILL. The -r flag on xargs ensures that kill -9 is only invoked if there are matching PIDs. The 2>&1 redirects stderr to stdout for clean output capture, and the appended echo "EXIT: $?" provides an unambiguous exit status.

The output is simply EXIT: 0, confirming that all Python processes were terminated successfully. This step is essential because SGLang servers bind to GPU memory and specific ports. Attempting to launch a new server while an old one holds GPU allocations would fail with CUDA out-of-memory errors or port conflicts. The assistant's earlier experience — documented in the game plan — had revealed that zombie worker processes often persist after server shutdown, requiring this aggressive cleanup approach.

Command 2: File Verification

ssh root@10.1.230.174 'ls -la /data/eagle3/aq_medai_k2/' 2>&1

This command lists the contents of the AQ-MedAI drafter directory on the container. The output reveals a directory with two relevant files: config.json (940 bytes) and model.safetensors (2,382,319,104 bytes, or approximately 2.38 GB). The presence of both files confirms that the earlier download (initiated in message 4929) completed successfully. The .cache subdirectory is an artifact of the Hugging Face Hub download process.

The file sizes are informative. The config.json at under 1 KB is a lightweight metadata file describing the architecture. The model.safetensors at 2.38 GB represents the full set of trainable weights: the feature compression layer (fc.weight), the single transformer layer (midlayer.*), the language model head (lm_head.weight), and normalization parameters. Notably absent from this file is the embed_tokens.weight — a 1.18 GB tensor mapping the 163,840-token vocabulary to 7168-dimensional embeddings — which the AQ-MedAI team excluded because it is loaded from the base model at runtime as a frozen weight.

Command 3: Configuration Inspection

ssh root@10.1.230.174 'cat /data/eagle3/aq_medai_k2/config.json' 2>&1

The output shows the JSON configuration, which the assistant partially displays (truncated at max_position_embeddin...). The visible fields confirm architectural compatibility:

Assumptions and Reasoning

The message operates on several implicit assumptions, each grounded in the extensive analysis that preceded it.

First assumption: That the AQ-MedAI drafter's weights can be loaded directly by SGLang's EAGLE-3 implementation without modification. This is supported by the earlier shape comparison (message 4931), which verified that every weight tensor in the AQ-MedAI file has an identical shape to its counterpart in the from-scratch K2.5 drafter. The only structural difference — the missing embed_tokens.weight — is handled automatically by SGLang, which loads this tensor from the base model.

Second assumption: That killing all Python processes is a safe and sufficient cleanup step. The assistant does not check for GPU memory leaks, lingering CUDA contexts, or stuck NCCL communicators. The game plan's accumulated wisdom — including the note about zombie workers persisting after server shutdown — justifies this aggressive approach. The EXIT: 0 output provides minimal but sufficient confirmation.

Third assumption: That the config.json file is complete and uncorrupted. The assistant reads only the beginning of the file (the output is truncated by the tool's display), trusting that the file was downloaded correctly from Hugging Face. Given that the download completed without errors and the file size (940 bytes) is consistent with a valid JSON configuration, this is a reasonable assumption.

Fourth assumption: That the hidden state representations at layers 2, 30, and 58 are sufficiently similar between K2 and K2.5 for the drafter to produce reasonable draft tokens. This is the central hypothesis being tested by Phase 0, and it is not yet validated. The assistant is about to find out whether the shared architecture translates to shared internal representations.

Input Knowledge Required

To fully understand this message, one must be familiar with several pieces of prior knowledge:

  1. The EAGLE-3 architecture: An auxiliary draft model that predicts multiple future tokens in parallel, using hidden states from intermediate layers of the target model as input features. The draft model consists of a feature compression layer (fc), a single transformer layer (midlayer), and a language model head (lm_head).
  2. The AQ-MedAI K2 drafter: A pre-trained EAGLE-3 draft model for Kimi-K2, trained on 1.4 million samples, achieving an acceptance length of 3.2–3.5 tokens. Downloaded in a previous message and stored at /data/eagle3/aq_medai_k2/.
  3. The infrastructure topology: A Proxmox host (10.1.2.6) managing an LXC container (10.1.230.174) with eight NVIDIA RTX PRO 6000 Blackwell GPUs connected via PCIe without NVLink. The absence of NVLink is the root cause of the high NCCL all-reduce latency that makes speculative decoding challenging.
  4. The game plan: The comprehensive eagle-k2finetune-game-plan.md document that outlines the three-phase strategy (Phase 0: quick probe, Phase 1: fine-tune on existing data, Phase 2: scale up training data).
  5. The cleanup protocol: The two-step kill procedure (first from the Proxmox host via pct exec, then fuser -k /dev/nvidia*) that was established after earlier sessions revealed zombie processes.

Output Knowledge Created

This message produces three concrete pieces of output knowledge:

  1. Confirmation of process cleanup: All Python processes on the container have been terminated, freeing GPU memory and ports for the new server launch. The exit code 0 provides unambiguous success confirmation.
  2. Confirmation of file availability: The AQ-MedAI drafter files exist at the expected path with the expected sizes. The config.json (940 bytes) and model.safetensors (2.38 GB) are both present and accessible.
  3. Confirmation of architectural compatibility: The config.json fields visible in the truncated output match the K2.5 architecture exactly. The hidden state layer IDs are [2, 30, 58], the vocabulary sizes match, and the model dimensions are identical. These three outputs collectively clear the way for the next step: launching the SGLang server with the AQ-MedAI drafter and measuring its acceptance length on K2.5 prompts. The message does not yet produce any performance data — that will come in subsequent messages as the server starts and benchmarks run.

The Thinking Process

The assistant's reasoning in this message follows a clear pattern: prepare the environment before introducing the variable. The logic is:

  1. The experiment requires a clean SGLang server instance.
  2. A clean server requires no competing Python processes holding GPU memory.
  3. Therefore, kill all Python processes first.
  4. While the kill runs, verify that the experimental artifact (the AQ-MedAI drafter) is present and correctly configured.
  5. If either step fails, the user needs immediate visibility into the failure. This is textbook experimental methodology. The assistant does not assume the drafter files are still present from the download two days earlier — it explicitly checks. It does not assume the old server has been shut down — it explicitly kills processes. It does not assume the config is correct based on memory — it reads the file fresh. The parallel execution of the three commands is also noteworthy. The assistant could have run them sequentially: kill, then check files, then read config. But by batching them, it reduces the round-trip latency from three SSH connections to one. This optimization matters in a session where each round may take seconds due to network latency and command execution time.

Conclusion

Message 4943 is a deceptively simple message that belies the depth of analysis behind it. On its surface, it is merely three bash commands: kill processes, list files, read a config. But in the context of the broader session, it represents the transition from analysis to action — the moment when weeks of debugging, profiling, and planning crystallize into a single experiment. The assistant is about to discover whether the AQ-MedAI K2 drafter, trained on 1.4 million samples from a related model, can transfer its knowledge to K2.5. The answer to that question will determine whether the team pursues fine-tuning or pivots to system-level optimizations of the verify step.

The message also demonstrates a mature engineering approach to experimental design: clean the environment, verify your artifacts, confirm your assumptions, and only then introduce the experimental variable. It is a small step, but it is the first step on a path that will ultimately determine whether EAGLE-3 speculative decoding can deliver on its promise of faster inference for one of the largest open-weight language models in existence.