supergemma4-26b-dflash-pilot

SuperGemma4-26B DFlash Draft (pilot / PoC)

This is a proof-of-concept DFlash block-diffusion drafter trained against AEON-7/supergemma4-26b-abliterated-multimodal-nvfp4 (the NVFP4-quantized SuperGemma4 26B Abliterated Multimodal model, bf16 twin).

What is this?

A small draft model used for speculative decoding: instead of the big 26B target generating one token per step, the drafter proposes multiple tokens in parallel using block diffusion, and the target verifies them in a single pass. This can 2-3× generation throughput once the drafter is accurate enough.

This release is a pilot (5K samples, 1 epoch, ~28 min on 1× RTX PRO 6000 Blackwell). Top-1 match rate with the target is only ~5.8%, too low to actually speed up generation. The artifact exists to validate the full training + export + deployment pipeline end-to-end. See Roadmap below.

Architecture

Training

Training infra notes

Built with NVIDIA/TensorRT-Model-Optimizer#1211 (DFlash training mode, merged in modelopt 0.43.0rc2.dev). Required two local patches:

1. EagleTrainerWithAccLog._save override to save drafter-only (1.2 GB) instead of the full 50 GB frozen target + drafter tree — stock HF Trainer would OOM the filesystem.
2. modeling_gemma4.py line 2027 — bypass the mm_token_type_ids required when training check for text-only training.

Deployment with vLLM

vllm serve AEON-7/supergemma4-26b-abliterated-multimodal-nvfp4 \
    --speculative-config '{
        "method":"draft_model",
        "model":"AEON-7/supergemma4-26b-dflash-pilot",
        "num_speculative_tokens":5
    }' \
    --quantization modelopt \
    --max-model-len 65536

⚠️ With only 5.8% top-1, expect negative speedup (verification overhead swamps any gain). This pilot is for plumbing validation, not perf.

Roadmap

This pilot proves the training stack. To make it actually fast:

Production-quality domain-general data (mix of ShareGPT, UltraChat, Magpie, LMSYS Chat, code) rather than UltraChat alone is also key.

Files

• model.safetensors — 1.2 GB, 58 tensors (5-layer drafter)
• config.json — Qwen3-style DFlashDraftModel config
• tokenizer.json, tokenizer_config.json, chat_template.jinja — from target
• trainer_state.json — loss/acc history per step
• training_args.bin — exact training config for reproducibility

License

Apache 2.0. This is derived work from the SuperGemma4 26B target.

---

☕ Support the work

AEON-7 models, drafters and tools are built and trained independently, on my own hardware. If they're useful to you, please consider supporting development:

Become a member on Patreon → patreon.com/cw/AeonForge7/membership

Milestones unlock bigger work: reaching 500 paid supporters will fund fine-tuning larger models and bigger project releases. Supporters also get early access to new releases, such as the AEON DFlash2 drafter.

If this release has been useful, tips are deeply appreciated — they go directly toward more compute, more models, and more open releases.

<table align="left"> <tr><td align="left"> <strong>₿ Bitcoin (BTC)</strong><br/> <img src="https://raw.githubusercontent.com/AEON-7/AEON-7/main/assets/qr/btc.png" alt="QR" width="200"/><br/> _{<code>bc1q09xmzn00q4z3c5raene0f3pzn9d9pvawfm0py4</code>} </td></tr> <tr><td align="left"> <strong>Ξ Ethereum (ETH)</strong><br/> <img src="https://raw.githubusercontent.com/AEON-7/AEON-7/main/assets/qr/eth.png" alt="QR" width="200"/><br/> _{<code>0x1512667F6D61454ad531d2E45C0a5d1fd82D0500</code>} </td></tr> <tr><td align="left"> <strong>◎ Solana (SOL)</strong><br/> <img src="https://raw.githubusercontent.com/AEON-7/AEON-7/main/assets/qr/sol.png" alt="QR" width="200"/><br/> _{<code>DgQsjHdAnT5PNLQTNpJdpLS3tYGpVcsHQCkpoiAKsw8t</code>} </td></tr> <tr><td align="left"> <strong>ⓜ Monero (XMR)</strong><br/> <img src="https://raw.githubusercontent.com/AEON-7/AEON-7/main/assets/qr/xmr.png" alt="QR" width="200"/><br/> _{<code>836XrSKw4R76vNi3QPJ5Fa9ugcyvE2cWmKSPv3AhpTNNKvqP8v5ba9JRL4Vh7UnFNjDz3E2GXZDVVenu3rkZaNdUFhjAvgd</code>} </td></tr> </table>

Ethereum L2s (Base, Arbitrum, Optimism, Polygon, etc.) and EVM-compatible tokens can be sent to the same Ethereum address.