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jayyun98/MiniCPM5-2B-SFT-Draftfit-DFlash2

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Model Card

Draftfit — MiniCPM5-2B-SFT DFlash2

Draftfit trains and adapts speculative-decoding draft models for a frozen target LLM.

This checkpoint is the DFlash2 branch adapted to:

Each Draftfit family has separate weights. DSpark, DFlash CE, DFlash DPACE and DFlash2 are not one universal checkpoint.

Validation set: 53 held-out conversations

Draft	PerfectBlend	OpenCode	APIGen-MT	Nemotron Agentic
Official DSpark	107.83	112.19	107.79	85.86
Draftfit DSpark	118.29	122.28	116.04	91.99
Draftfit DFlash CE	89.02	99.18	92.49	79.31
Draftfit DFlash DPACE	89.84	103.20	91.62	76.97
Draftfit DFlash2	78.37	92.76	87.49	70.41

Full prompt-free summary: `evaluation-v4.json`

Draft	Best use
DSpark	Best first choice when adapting an existing DSpark draft to a new target
DFlash CE	Simple block-parallel DFlash baseline
DFlash DPACE	Test position-weighted training objectives against CE
DFlash2	Research richer candidate ranking / grouped-convolution drafts

For this MiniCPM5-SFT experiment, DSpark is the strongest practical baseline.

Draftfit DSpark reached 116.15 tok/s vs 105.38 tok/s for the official DSpark in the tested MLX setup.

These measurements do not establish a general speed advantage.

Revision	Description
`main`	DFlash2, primary release
`v5`	Additional 1,000-step continual-training stage on a fresh split

Different revisions used different held-out sets, so their measurements should not be used to rank revisions directly.

The models were trained for 1,000 steps per stage using disjoint 1,000-conversation training sets.

Training mixture per stage:

Important limitations:

—DSpark/DFlash serving correctness still requires exact token/state and acceptance-length validation.
—DFlash2 was tested on MLX but not with SGLang 0.5.16.
—A successfully loaded draft checkpoint does not prove correct speculative decoding.
—Reported speed is specific to the tested target, hardware, verifier settings and prompt set.

bash

hf download jayyun98/MiniCPM5-2B-SFT-Draftfit-DFlash2 \
  --revision main \
  --local-dir ./draft

The repository contains:

text

config.json
model.safetensors

Use them with the pinned MiniCPM5-2B-SFT target and tokenizer.

Training, conversion and benchmarking workflows are documented in:

The training mixture includes APIGen-MT-5k, which is marked CC BY-NC 4.0 and includes additional generator-provenance restrictions.

Treat this release as research / non-commercial unless you have reviewed the source dataset terms.

See `SOURCE_TERMS.md` and release_manifest.json.

Experimental research artifact. The reported numbers are not a general production speedup or correctness claim.