Ling3-DSpark
A DSpark speculator for Ling3. DSpark extends DFlash with target-model auxiliary features and a confidence head that dynamically chooses the number of draft tokens. The model was trained with SpecForge and is served with SGLang.
Model specifications
- Target model: Ling-3.0-flash
- Draft parameters: 1,363,707,905 (1.36B)
- Draft weight dtype: BF16
- Hidden size: 2,560
- Transformer layers: 5 full-attention layers
- Attention: MHA with 32 query heads and 32 key/value heads
- Target auxiliary feature layers: 1, 11, 23, 29, 35
- Confidence head: vanilla Markov head, rank 256
- DSpark block size: 8 draft tokens (verify width 9, including the target bonus token)
- Maximum position embeddings: 262,144
Acceptance length
Acceptance length is the mean number of tokens accepted per speculative verification step, including the target bonus token.
| Workload | Acceptance length |
|---|---|
| GSM8K | 6.40 |
| MATH-500 | 6.29 |
| AIME 2025 | 5.56 |
| HumanEval | 6.57 |
| MBPP | 6.34 |
| LiveCodeBench | 5.33 |
| MT-Bench | 3.92 |
| Alpaca | 3.51 |
| Arena-Hard-v2 | 3.72 |
The macro mean across the nine workload means is 5.29.
Serving with SGLang
Launch recipes for this draft on every supported hardware/quantization cell — including the required --linear-replayssm-cache-len sizing — with measured speed and accuracy, are in the SGLang Ling-3.0-flash cookbook.
Use an SGLang version with DSPARK support. Replace the model paths and tensor-parallel size with values appropriate for your deployment:
sglang serve \
--trust-remote-code \
--model-path <LING3_MODEL_PATH> \
--tp-size <TP_SIZE> \
--speculative-algorithm DSPARK \
--speculative-draft-model-path <LING3_DSPARK_MODEL_PATH> \
......
Serving with llama.cpp
Use a llama.cpp build with DSpark support. Replace the model paths, quantization type, and GPU layer counts with values appropriate for your deployment. First convert and quantize the target model:
python convert_hf_to_gguf.py path/to/Ling-3.0-flash \
--outfile path/to/Ling-3.0-flash-bf16.gguf \
--outtype bf16 --model-name Ling-3.0-flash
llama-quantize \
path/to/Ling-3.0-flash-bf16.gguf \
path/to/Ling-3.0-flash-Q4_K_M.gguf \
Q4_K_M
Then generate the DSpark draft GGUF:
python convert_hf_to_gguf.py \
path/to/Ling-3.0-flash-dspark \
--target-model-dir path/to/Ling-3.0-flash \
--outtype bf16 \
--outfile path/to/Ling-3.0-flash-DSpark.gguf
Finally, launch the server with the DSpark draft as the speculative model:
llama-server \
--model path/to/Ling-3.0-flash-Q4_K_M.gguf \
--spec-draft-model path/to/Ling-3.0-flash-DSpark.gguf \
--spec-type draft-dspark --spec-draft-n-max 8 \
-ngl all -ngld all -fa on