Model reference · open weights
Fast-dDrive is an open-weight language model from Efficient-Large-Model, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
Fast-dDrive Fast-dDrive is a block-diffusion Vision-Language-Action (VLA) model for end-to-end autonomous driving, built on Qwen2.5-VL-3B. It pairs section-aware structured-diffusion training (SASD) with scaffold-aware speculative decoding (Scaffold Spec) and an optional shared-prefix multi-trajectory inference scaling scheme, and reaches SOTA accuracy on the Waymo Open Dataset End-to-End Driving (WOD-E2E) benchmark at over 200 tokens / second on a single H100. Quick start Inference paths This release exposes three decoding paths as bound methods on the model: Important: scaffoldspeculativesample and its multi-traj variant must be run with confidencethreshold=0.0 to reproduce the paper numbers. Running at 0.9 silently degrades both ADE and throughput. Headline results — WOD-E2E test set (single H100) On the WOD-E2E val set, Scaffold Spec runs at 1919 ms / sample (4.1× over the AR baseline); fused with SGLang the same configuration drops to 665 ms / sample at 608.5 TPS — the 11.8× / 12× speedup over AR cited in the paper. Files - modeling.py — model definition (FastdDriveForConditionalGeneration) - configuration.py — config classes - sectionutils.py — scaffold construction + section-aligned block index utilities - generationutils.py — the three inference paths, attached to the model class on import - config.json, generationconfig.json, preprocessorconfig.json, chattemplate.jinja, tokenizer files — standard HF artifacts - model-0000{1..4}-of-00004.safetensors — model weights (4 shards) Citation
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | Efficient-Large-Model |
|---|---|
| Type | Language models |
| Parameters (lead) | 235M |
| Variants | 1 |
| Runs with | transformers |
| Released | 2026-05-30 |
| Popularity | 68 downloads / month |
| Likes | 3 |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| Fast-dDrive | 235M | BF16 | ~0.5 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys fast-ddrive for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (fast-ddrive below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"fast-ddrive","messages":[{"role":"user","content":"Hello"}]}'
Licence
Open weights under apache-2.0 — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗