linne-fern-1.0

linne-fern-1.0 is a 27B decision model. Give it a state (text, a document, JSON, a conversation or an agent trajectory) and a question with a set of options, and it returns one option together with a probability for every option, in a single forward pass.

It is built for the decisions that sit inside real products and agent loops: classification, routing, fact checking, choosing the right tool response, and approving what an agent is about to do.

Highlights

• Decision Index public score 58.91, measured with the official kit (edition 0.3, all 37 public benchmarks, 140,620 requests).
• Broad: knowledge, language understanding, retrieval and classification, tools and automation, in English, Vietnamese and Arabic.
• Agent-safe: strong step-level guard for agents (97.7% caught at 0.3% false blocks on our held-out agent steps) and a low false-block rate on benign prompts.
• Fast: one forward pass per decision, about 46 ms p50 on a single H200.
• Drop-in: standard Qwen3.8 architecture and chat template; runs on transformers, vLLM and SGLang.

Decision Index (public part, edition 0.3)

Chance-corrected skill, higher is better. All rows measured by us with the official kit and the same serving path, except the last row, which is the public-part score published on the leaderboard.

Only the public part (20% of the full leaderboard score) can be measured outside the leaderboard; the full score also includes private tests run by the maintainers.

Safety and agent benchmarks

Usage

The model answers by putting probability on the option letter that follows a fixed prompt: the state, a blank line, the question, one line per option (A: name - description) and Answer with the letter of one option only. Use the chat template with thinking disabled and read the next-token probabilities of the option letters.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

name = "beyoru/linne-fern-1-0"
tok = AutoTokenizer.from_pretrained(name)
model = AutoModelForCausalLM.from_pretrained(name, dtype=torch.bfloat16, device_map="auto")

state = "My Stripe integration keeps failing. Please help ASAP."
question = "Which team should handle this request?"
options = {"billing": "Charges and refunds", "technical_support": "Integration errors", "sales": "Questions about buying a product"}

letters = [chr(65 + i) for i in range(len(options))]
lines = [f"{l}: {k} - {v}" for l, (k, v) in zip(letters, options.items())]
user = "\n".join([state, "", f"Question: {question}", *lines, "Answer with the letter of one option only."])
prompt = tok.apply_chat_template([{"role": "user", "content": user}], tokenize=False,
                                 add_generation_prompt=True, enable_thinking=False)
ids = tok(prompt, return_tensors="pt", add_special_tokens=False).to(model.device)
with torch.no_grad():
    logits = model(**ids).logits[0, -1]
label_ids = [tok(prompt + l, add_special_tokens=False)["input_ids"][-1] for l in letters]
probs = torch.softmax(logits[label_ids].float(), dim=0)
print({k: round(float(p), 4) for k, p in zip(options, probs)})

It also works as a regular chat model, and with structured output (JSON with an enum field, thinking disabled) for typed decisions.

License

Apache 2.0, same as the base model.