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Laguna-S-2.1-DSpark
orestis-z/Laguna-S-2.1-DSpark
Laguna-S-2.1-DSpark is a machine learning model from orestis-z. 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 speculators. The card lists the license as apache-2.0.
DSpark speculative decoding drafter for poolside/Laguna-S-2.1 (117.6B MoE), trained with speculators.
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At a glance
- Library
- speculators
- License
- apache-2.0
- Access
- Public
- Created
- Aug 6, 2026
- Updated
- Aug 13, 2026
- SHA
- 78ec3a49
Base models
Datasets
Model card
From the Hugging Face model README
Laguna-S-2.1-DSpark
DSpark speculative decoding drafter for poolside/Laguna-S-2.1 (117.6B MoE), trained with speculators.
Training
Interactive training metrics dashboard
- Warm-started from the published DFlash checkpoint poolside/Laguna-S-2.1-DFlash, converted to DSpark format
- Dataset: On-policy regeneration of mlabonne/open-perfectblend with Laguna-S-2.1 (RedHatAI/opb-laguna-s)
- Hardware: 8x H200 GPUs (4 for vLLM serving with TP4, 4 for training)
- Epochs: 3
- Loss:
{"ce": 0.1, "tv": 0.9} - Learning rate: 1e-4
- Sequence length: 8192 tokens, max 3072 anchors
- Markov head: rank 256, vanilla
- Confidence head: enabled, with Markov, alpha=1.0
Best checkpoint validation metrics
| Metric | Value |
|---|---|
| val/accept_len | 6.780 |
| val/accept_rate | 0.634 |
| val/loss | 0.206 |
| val/ce_loss | 0.359 |
| val/tv_loss | 0.078 |
| val/position_1_acc | 0.857 |
| val/full_acc | 0.665 |
Evaluation (guidellm acceptance length)
Compared against the baseline poolside/Laguna-S-2.1-DFlash:
| Subset | DFlash | DSpark | Delta |
|---|---|---|---|
| HumanEval | 2.236 | 2.624 | +17.4% |
| math_reasoning | 3.150 | 3.346 | +6.2% |
| qa | 2.167 | 2.298 | +6.1% |
| question | 2.219 | 2.487 | +12.1% |
| rag | 2.277 | 2.468 | +8.4% |
| summarization | 2.177 | 2.239 | +2.8% |
| tool_call | 2.568 | 2.647 | +3.1% |
| translation | 2.948 | 2.704 | -8.3% |
| writing | 2.250 | 2.466 | +9.6% |
| Average | 2.443 | 2.586 | +5.8% |
Usage with vLLM
vllm serve poolside/Laguna-S-2.1 \
--speculative-config '{"method": "dspark", "model": "orestis-z/Laguna-S-2.1-DSpark", "num_speculative_tokens": 15}'
Files
config.json,config.py,model.safetensors— checkpoint filestrain_command.txt— full training command with versionstrackio/train_metrics.csv— per-step training metrics (81k steps)trackio/val_metrics.json— best checkpoint validation metrics
- Library
- speculators
- License
- apache-2.0
- Created
- Aug 6, 2026
- Updated
- Aug 13, 2026