Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/nvidia/model-optimizer/eagle3-new-modelnpx skills add NVIDIA/Model-Optimizer --skill eagle3-new-modelgit clone --depth 1 https://github.com/NVIDIA/Model-OptimizerWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00098 | $0.00665 |
| Opus 5 | $0.00049 | $0.00332 |
| Sonnet 5 | $0.00020 | $0.00133 |
| Haiku 4.5 | $0.00010 | $0.00067 |
Grade A, and why
eagle3-new-model scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EAGLE3 New Model Configuration
Create tools/launcher/examples/<Org>/<Model>/hf_offline_eagle3.yaml by copying the
closest existing example and adapting it. Pick a reference with the same shape as the
target (dense vs MoE, similar size) from tools/launcher/examples/ — e.g. the Qwen3-8B
config for a dense model.
The pipeline is a 4-task config (task_0 data synthesis → task_1 hidden-state dump →
task_2 train → task_3 benchmark). The task structure, args, containers, and GPU/node
sizing are all visible in the existing examples — infer them from a reference rather than
hand-rolling. This file documents only the two things that are not obvious from the
examples: which dump backend to pick, and the model-specific gotchas.
Choosing the task_1 hidden-state dump backend
| Backend | Script | When to use |
|---|---|---|
| vLLM | common/eagle3/dump_offline_data_vllm.sh |
Default. Broad coverage via vLLM's native hidden-state extractor. |
| HF | common/eagle3/dump_offline_data_hf.sh |
VLMs / multimodal, custom-code models, sliding-window attention (TRT-LLM can't serve these). |
| TRT-LLM | common/eagle3/dump_offline_data.sh |
Pure-text models with TRT-LLM support; pass --tp <TP> and --moe-ep <EP>. |
Rule of thumb: HF if the model is a VLM or uses sliding-window attention; vLLM otherwise. TRT-LLM only when you specifically want its kernels for a supported plain-text model.
Model-specific adjustments
These are the non-obvious knobs that vary per model:
| Situation | What to change |
|---|---|
Requires --trust-remote-code |
Add to task_0 vLLM args (before the -- separator) and to task_3 benchmark args |
| MoE with large expert hidden dim | Increase intermediate_size in eagle_config.json to match moe_intermediate_size |
| Custom tokenizer (e.g. tiktoken) | Set TIKTOKEN_RS_CACHE_DIR env var in task_0 and task_1 |
After adapting the config, preview it with --dryrun before submitting.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 47 lines · 98 tokens per session scan A d47d39626420
eagle3-new-model is a skill published in the GitHub repository NVIDIA/Model-Optimizer (3,675 stars, last pushed yesterday), licensed Apache-2.0. It adds 98 tokens to every session and 665 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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