Muse-Glimmer-30B-DSpark-Coding

Muse Glimmer 30B DSpark Coding

A coding-specialized DSpark drafter for Muse Glimmer 30B, fine-tuned from DaoCloud/Muse-Glimmer-30B-DSpark on long-horizon, on-policy software-engineering traces.

This is a speculative-decoding drafter, not a standalone language model. Pair it with Muse Glimmer 30B as the verifier. The default checkpoint at the repository root is the 49K-context run; the 32K-context comparison checkpoint is retained under checkpoints/32k-context.

Results

Evaluation uses Muse-Glimmer-Terminal-Bench-Eval, the same BF16 Muse Glimmer 30B verifier, greedy decoding, and 15 speculative tokens. The table below reports the valid per-request mean acceptance length and pooled decode throughput at concurrency 1. Higher is better.

Acceptance and throughput

Acceptance length is calculated per request as:

acceptance_length = 1 + accepted_draft_tokens / speculative_steps

The values above are then averaged over requests. Step-weighted acceptance is intentionally not reported.

Speedup across concurrency

Speedup is pooled decode throughput relative to the no-speculation control at the same concurrency.

These benchmarks measure decoding efficiency, not task correctness. Throughput depends on hardware, runtime revisions, prompt/output lengths, and serving configuration.

Training recipe

Checkpoints

Example: serve with vLLM

vllm serve meta-models/Muse-Glimmer-30B \
  --tensor-parallel-size 1 \
  --enable-auto-tool-choice \
  --tool-call-parser muse_glimmer \
  --reasoning-parser muse_glimmer \
  --speculative-config \
  '{"method":"dspark","model":"Satgoy152/Muse-Glimmer-30B-DSpark-Coding","num_speculative_tokens":15}'

Notes

• The model was trained for coding and agentic workloads. General-chat, multilingual, and multimodal speculative decoding have not been comprehensively evaluated.
• The checkpoint was trained and evaluated with development versions of vLLM and Speculators. Runtime support may vary by release.
• The confidence head is included in the checkpoint, but the evaluated runtime does not use it for confidence-scheduled verification.
• Reproduction, training, evaluation, and serving code: Satgoy152/muse-glimmer-dspark.

Released under Apache-2.0, matching Muse Glimmer and the warm-start checkpoint.