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PNPM244
Available on FriendliAI
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Model Details
Model Provider
PNPM244
Model Tree
google/gemma-4-E4B-it
Input Modalities
Output Modalities
Supported Functionality
Chance-corrected skill, higher is better. linne rows measured by us with the official kit.
| Model | Overall | Knowledge | Language | Retrieval | Tools | Arts |
|---|---|---|---|---|---|---|
| linne-oak-1.0 (31B) | 63.94 | 54.3 | 66.6 | 69.1 | 78.5 | 45.1 |
| linne-fern-1.0 (27B) | 58.92 | 44.9 | 62.8 | 62.4 | 79.0 | 41.5 |
| linne-sprout-1.0 (E4B) | 48.88 | 29.2 | 54.9 | 55.5 | 70.3 | 31.8 |
Only the public part (20% of the full leaderboard score) can be measured outside the leaderboard.
The prompt is one user message (chat template, thinking off): the state (compact JSON when not a string), a blank line,
Question: <instructions>, one line <label>: <name> - <description> per option, and
Answer with the letter of one option only. The answer is the softmax over the next-token logits of the option letters
(with the model's final logit soft-capping), read at the last prompt position, with the full-attention layers bidirectional.
linne_engine.py (with nc_hook.py) in this repository implements exactly this, as a Decision Index engine and as a library:
python
import sysfrom huggingface_hub import snapshot_download path = snapshot_download("MISA-core/linne-sprout-1-0")sys.path.insert(0, path)from linne_engine import LinneEngine model = LinneEngine(model=path, device="cuda:0", noncausal=True)result, _ = model( "My Stripe integration keeps failing. Please help ASAP.", {"team": {"type": "choice", "instructions": "Which team should handle this request?", "criteria": {"billing": "Charges and refunds", "technical_support": "Integration errors", "sales": "Questions about buying a product"}}},)print(result["answers"]["team"]) # chosen option and a probability for every optionRun the Decision Index public suite:
text
PYTHONPATH=<path> python -m decision_index pipeline --engine linne_engine:LinneEngine \ --option model=<path> --option device=cuda:0 --option noncausal=1 --out runs/linne-sprout-1-0BF16 weights are about 16 GB and fit one 24 GB GPU.
Apache 2.0, following the base model's license.
import sysfrom huggingface_hub import snapshot_download path = snapshot_download("MISA-core/linne-sprout-1-0")sys.path.insert(0, path)from linne_engine import LinneEngine model = LinneEngine(model=path, device="cuda:0", noncausal=True)result, _ = model( "My Stripe integration keeps failing. Please help ASAP.", {"team": {"type": "choice", "instructions": "Which team should handle this request?", "criteria": {"billing": "Charges and refunds", "technical_support": "Integration errors", "sales": "Questions about buying a product"}}},)print(result["answers"]["team"]) # chosen option and a probability for every optionPYTHONPATH=<path> python -m decision_index pipeline --engine linne_engine:LinneEngine \ --option model=<path> --option device=cuda:0 --option noncausal=1 --out runs/linne-sprout-1-0