{
  "backend_type": "opencl",
  "thread_num": 4,
  "precision": "low",
  "memory": "low",
  "sampler_type": "greedy",
  "speculative_type": "draftmodel",
  "draft_predict_length": 3,
  "draft_config_path": "/path/to/config_cpu.json"
}

Status

This is currently best treated as a research / comparison artifact:

useful if you want to compare 20K vs 40K
not yet the clearest device-side winner over the simpler 20K drafts

Limitations and Intended Use

This is a research comparison artifact first.
We do not currently have stronger preserved device evidence for this variant than for the simpler 20K drafts.
Mobile performance still depends more on target pairing and backend routing than on a small training-objective delta alone.

Collection

TokForge Mobile Draft Models

Best-known use

Draft model backend: CPU
Draft threads: 2
Draft predict length: d=3
Typical target pairing: Qwen3-8B

Included files

llm.mnn
llm.mnn.weight
llm_config.json
config.json
config_cpu.json
tokenizer files
ONNX export artifact for reference

TokForge

Website: tokforge.ai
Discord: Join the Discord

If you benchmark this on your own device, feel free to share results in Discord.

darkmaniac7/Qwen3-0.6B-lk-alpha-40k-MNN

Qwen3-0.6B-lk-alpha-40k-MNN

Why this repo exists

Training snapshot

Usage

Status

Limitations and Intended Use

Collection

Best-known use

Included files

TokForge

darkmaniac7/
Qwen3-0.6B-lk-alpha-40k-MNN