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Papajams
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
Papajams
Model Tree
Qwen/Qwen3.5-9B
Input Modalities
Output Modalities
Supported Functionality
| Metric | Value |
|---|---|
| Win rate vs base (best checkpoint) | 52.56% |
| Iterations completed | 5 / 5 |
| Adaptation quality grade | A |
| Training rows (adapted dataset) | 27,965 |
| Real seed rows (production DB) | 1002 |
Win rate is Adaption's head-to-head metric: the share of held-out evals the adapted model beats the base model on. 52.56% means the adapter wins ~53 of every 100 comparisons — a modest but real, measurable improvement.
Research artifact + challenge submission. Given a signal's evidence payload (commit message, release notes, diff patch, or synthesis narrative) it produces a JSON object like:
json
{"detector_type": "protocol_upgrade", "recommended_action": "review_before_mainnet", "confidence": 0.78, "price_direction": "up", "horizon": "24h"}
Qwen/Qwen3.5-9B4b0ef68e-a233-454b-87d5-203f5c9d401c · Seed dataset ID: c701c50c-7582-4be1-9d7c-62618a001738