Instructions to use RESMP-DEV/DFlash2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use RESMP-DEV/DFlash2 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download RESMP-DEV/DFlash2 --local-dir DFlash2
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
DFlash2
This repository contains the promoted DFlash2 draft head used with
Qwen/Qwen3.8-27B. It is not a
fine-tune of the full Qwen target and must not be loaded as a standalone
language model. It has no tokenizer and no target weights: a compatible
DFlash2 runtime loads this head alongside the Qwen verifier.
The Hub base-model tree is intentionally omitted. A conventional "Qwen finetune" label would misrepresent the artifact: only the small speculative draft head is changed, while target-controlled output remains the authority.
The head descends from the public Qwen3.8-27B DFlash2 draft model. It was
adapted with target-verified cycles from the incumbent legacy corpus and a
session-grouped Fable agentic corpus. The final checkpoint combines a
generation-2 Fable adaptation at strength 0.0625 with a generation-3
combined-domain adaptation at strength 0.1875.
No trace dataset, prompt file, capture cycle, selector tensor, or W&B log is included in this repository. Only the final draft checkpoint, configuration, blend metadata, and evaluation summary are published.
Acceptance results
All acceptance runs used greedy target-controlled generation on one Apple M4
Max through DFlash MLX. The target was the calibrated Qwen3.8-27B MXFP4
runtime. Draft quantization was disabled for this head.
| Paired gate | Incumbent | This head | Mean delta | Wins / ties / losses |
|---|---|---|---|---|
| Legacy 24-prompt corpus | 0.6712239583 |
0.6718750000 |
+0.0006510417 |
5 / 14 / 5 |
| Fable 25-prompt held-out | 0.6471875000 |
0.6492187500 |
+0.0020312500 |
11 / 5 / 9 |
The Fable live-gate prompts were excluded from the generation-2 and combined training splits. The legacy corpus had already been used to train the incumbent, so it remained in the combined training capture for a paired comparison.
Exact parity
The exact 256-token baseline-versus-DFlash smoke gate passed:
token_match:true- smoke acceptance:
0.7109375 - diagnostic baseline throughput:
21.9615tok/s - diagnostic DFlash throughput:
33.5111tok/s
Throughput is diagnostic only. Thermal pressure and co-tenant machine load were not controlled.
Training summary
- combined capture: 11,273 target-verified cycles
- prompt groups: 127
- legacy contribution: 1,981 cycles from 24 prompts
- Fable contribution: 9,292 cycles from 103 training prompts
- excluded Fable live gate: 25 prompts
- teacher signal: target top-64 logits and greedy target tokens
- final full-run held-out prefix accuracy:
0.7932212528 - final full-run held-out top-1 accuracy:
0.8714877986
The capture teacher was the calibrated MXFP4 Qwen3.8-27B target runtime, not the pinned BF16 Qwen3.8-27B checkpoint. This precision choice is part of the training provenance.
Checkpoint integrity
model.safetensorsSHA-256:c90e0fc65dddbe4107b1b417d771c3be188c87197e89cf4b14415f56abc866f0config.jsonSHA-256:873e3556509b0da06e29654ba00d4944888d4b5e8a33afde25f7eb27d321e980blend.jsonSHA-256:e53a6bb5e890d7e3368053302189766c3ef68d2eb7887152ece1ea9eac4f64d5
Intended use and limits
This artifact is for lossless speculative decoding research with a compatible DFlash2 implementation. Acceptance metrics do not establish broad task quality, natural-EOS behavior, multilingual performance, long-context performance, or a controlled thermal throughput win. The Fable held-out prompts come from the same eval shard as the Fable training prompts, so that gate does not prove out-of-domain generalization.
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