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Muse-Glimmer-30B-DFlash2-Coding
Satgoy152/Muse-Glimmer-30B-DFlash2-Coding
Muse-Glimmer-30B-DFlash2-Coding is a machine learning model from Satgoy152. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
A coding-specialized DFlash2 drafter for Muse Glimmer 30B, fine-tuned from the converted z-lab/Muse-Glimmer-30B-DFlash2 checkpoint on long-horizon, on-policy software-engineering traces.
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At a glance
- Library
- transformers
- License
- apache-2.0
- Access
- Public
- Created
- Sep 6, 2026
- Updated
- Sep 9, 2026
- SHA
- b908a62e
Base models
Model card
From the Hugging Face model README
Muse Glimmer 30B DFlash2 Coding
A coding-specialized DFlash2 drafter for Muse Glimmer 30B, fine-tuned from the converted z-lab/Muse-Glimmer-30B-DFlash2 checkpoint 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 final checkpoint is at the repository root; the mid-training checkpoint is retained under checkpoints/mid-step-1976.
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
| Drafter | Acceptance length (per request) | Change vs DFlash2 | Pooled tok/s | Change vs DFlash2 | Speedup vs no speculation |
|---|---|---|---|---|---|
| DFlash (official) | 4.436 | −29.7% | 190.9 | −23.5% | 3.06× |
| DSpark (community) | 4.022 | −36.2% | 183.9 | −26.3% | 2.95× |
| DFlash2 | 6.306 | reference | 249.6 | reference | 4.00× |
| This model — mid (step 1,976) | 7.266 | +15.2% | 259.7 | +4.0% | 4.16× |
| This model — final (step 3,956) | 7.350 | +16.6% | 265.9 | +6.5% | 4.26× |
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.
| Drafter | c1 | c2 | c8 | c32 |
|---|---|---|---|---|
| DFlash (official) | 4.04× | 3.69× | 3.34× | 1.05× |
| DSpark (community) | 2.95× | 2.82× | 2.48× | 1.31× |
| DFlash2 | 4.00× | 3.93× | 3.39× | 1.79× |
| This model — mid (step 1,976) | 4.17× | 4.04× | 3.43× | 1.85× |
| This model — final (step 3,956) | 4.26× | 4.02× | 3.57× | 1.89× |
These benchmarks measure decoding efficiency, not task correctness. Throughput depends on hardware, runtime revisions, prompt/output lengths, and serving configuration.
Training recipe
| Setting | Value |
|---|---|
| Verifier | meta-models/Muse-Glimmer-30B (BF16) |
| Initialization | Converted z-lab/Muse-Glimmer-30B-DFlash2 checkpoint |
| Training data | Satgoy152/Muse-Glimmer-SWE-Gym-2k |
| Evaluation data | Satgoy152/Muse-Glimmer-Terminal-Bench-Eval |
| Hidden-state layers | [2, 14, 26, 38, 50] |
| DFlash2 heads | Convolution kernel 2, group size 16, selector rank 256, top-k 16 |
| Proposals | block_size=16, sample_from_anchor=false (15 speculative tokens) |
| Packed sequence length | 32,768 tokens |
| Loss | 0.1 CE + 0.9 TV; selector loss weight 1.0 |
| Optimizer | Muon for eligible 2D matrices; AdamW for the remaining parameters |
| Learning rate | Muon 1e-4; AdamW 5e-5 |
| Schedule | Cosine decay, 10% warmup |
| Training | 1 epoch, 4 trainer ranks |
Checkpoints
| Checkpoint | Location | Size | SHA-256 |
|---|---|---|---|
| Final, step 3,956 (default) | Repository root | 5,544,328,456 bytes | f520710be26f8ffaaa4151622391e9302c8a4b1b72e50a0283abe3ac7c9431d0 |
| Mid, step 1,976 | checkpoints/mid-step-1976 | 5,544,328,456 bytes | 09bd708bd15ca9d8326e5342185171e87dba7aef19dd910a535805bb658acbae |
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":"dflash2","model":"Satgoy152/Muse-Glimmer-30B-DFlash2-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.
- Reproduction, training, evaluation, conversion, and serving code: Satgoy152/muse-glimmer-dspark.
Released under Apache-2.0, matching Muse Glimmer and the warm-start checkpoint.
- Library
- transformers
- License
- apache-2.0
- Created
- Sep 6, 2026
- Updated
- Sep 9, 2026