Train + validate a speculative-decoding draft head (EAGLE3 or DFlash) for an ARBITRARY target LLM, generalizing the GLM-5.1-only SpecForge recipe so any new model gets one. Parameterizes what that offline recipe hard-codes: target path, aux-hidden-states layers (derived [1, L/2-1, L-4] from config.json numlayers), chat template, draft-head config, and method. Wires the SpecForge preparehiddenstates -> traineagle3 / traindflash -> convert-to-vLLM flow on Slurm, then gates on a measured acceptance-length A/B (dflashvllmeval.py + vLLM specdecode counters) vs the standing config. This is the train-spec-decode phase of inference-model-optimize, usable standalone. Triggers on "train an EAGLE3 draft", "train a DFlash head", "build a speculative decoder for ", "spec-decode draft training", "draft model acceptance", "SpecForge for ", "generalize eagle3-train", or any combination of "train / build / validate" with "eagle3 / dflash / draft / speculative / spec-decode".
Install with
Claude CodeCodexCursorGitHub CopilotGemini CLI
Supported by the Atlas install prompt. Skill-specific requirements vary.
Skill stats
Skills1CategoryProduct & designStars0
About this skill
Train + validate a speculative-decoding draft head (EAGLE3 or DFlash) for an ARBITRARY target LLM, generalizing the GLM-5.1-only SpecForge recipe so any new model gets one. Parameterizes what that offline recipe hard-codes: target path, aux-hidden-states layers (derived [1, L/2-1, L-4] from config.json numlayers), chat template, draft-head config, and method. Wires the SpecForge preparehiddenstates -> traineagle3 / traindflash -> convert-to-vLLM flow on Slurm, then gates on a measured acceptance-length A/B (dflashvllmeval.py + vLLM specdecode counters) vs the standing config. This is the train-spec-decode phase of inference-model-optimize, usable standalone. Triggers on "train an EAGLE3 draft", "train a DFlash head", "build a speculative decoder for ", "spec-decode draft training", "draft model acceptance", "SpecForge for ", "generalize eagle3-train", or any combination of "train / build / validate" with "eagle3 / dflash / draft / speculative / spec-decode".
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Train + validate a speculative-decoding draft head (EAGLE3 or DFlash) for an ARBITRARY target LLM, generalizing the GLM-5.1-only SpecForge recipe so any new model gets one. Parameterizes what that offline recipe hard-codes: target path, aux-hidden-states layers (derived [1, L/2-1, L-4] from config.json num_layers), chat template, draft-head config, and method. Wires the SpecForge prepare_hidden_states -> train_eagle3 / train_dflash -> convert-to-vLLM flow on Slurm, then gates on a measured acceptance-length A/B (dflash_vllm_eval.py + vLLM spec_decode_* counters) vs the standing config. This is the train-spec-decode phase of inference-model-optimize, usable standalone. Triggers on "train an EAGLE3 draft", "train a DFlash head", "build a speculative decoder for <model>", "spec-decode draft training", "draft model acceptance", "SpecForge for <model>", "generalize eagle3-train", or any combination of "train / build / validate" with "eagle3 / dflash / draft / speculative / spec-decode".
About this listing
Category
Product & design
Reported license
MIT
Listing language (estimated)
English
Review status
Not individually reviewed
Agent compatibility
Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLI