Bosun-XS (0.6B)

Launch post: Introducing Bosun →

The judge that keeps an agent's memory — its knowledge graph — clean. As an agent accumulates memory as a graph of facts linked by relationships, Bosun-XS decides, edge by edge, which connections are warranted — supported, non-redundant, still-true — so the graph stays useful instead of growing into noise that drowns the model reading it back. Nothing else scores that "judge" step; Bosun-XS is a small, fast, calibrated model built for it, and you program it with a sentence.

Given two findings and an instruction it emits P = sigmoid(logit_yes - logit_no) ∈ [0,1] — how strongly the pair satisfies the rule you supplied, with no opinion of its own. "Warranted" isn't one fixed rule (same-entity, cross-domain bridge, not-a-duplicate, still-supported-by-evidence), so you define it per graph; Bosun-XS follows the rule, respects negation, and generalizes to rules it never trained on. That same capability is exactly what RAG filtering, content moderation, and deduplication need too — knowledge-graph curation is simply where the need bites first and hardest.

LoRA fine-tune of Qwen/Qwen3-Reranker-0.6B, scored on the native reranker yes/no logits.

Inference contract

Native Qwen3-Reranker template; read the last-token logits:

<Instruct>: <your rule, e.g. "Connected only if the two findings share a specific named entity.">
<Query>: These two findings share the specified relationship.
<Document>: FINDING A:\n<text_a>\n\nFINDING B:\n<text_b>

score = sigmoid(logits[yes_id] - logits[no_id]) at the final position (logits_to_keep=1). The exact yes_id / no_id / template prefix+suffix and max_len are in serving.json.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

repo = "Hanno-Labs/bosun-xs"
cfg  = ...  # serving.json from this repo
tok  = AutoTokenizer.from_pretrained(repo, subfolder="tokenizer", padding_side="left")
base = AutoModelForCausalLM.from_pretrained(cfg["base_model"], torch_dtype=torch.bfloat16,
                                            attn_implementation="sdpa", trust_remote_code=True)
model = PeftModel.from_pretrained(base, repo).merge_and_unload().eval().cuda()
# build ids = prefix + <Instruct/Query/Document> + suffix, then:
# lg = model(input_ids, attention_mask, logits_to_keep=1).logits[:, -1, :]
# p  = torch.sigmoid(lg[:, cfg["yes_id"]] - lg[:, cfg["no_id"]])

Run locally (GGUF / llama.cpp)

CPU / Apple-Silicon / edge builds (f16, Q8_0, Q4_K_M) live at Hanno-Labs/bosun-xs-GGUF.

⚠️ Do not use llama.cpp's --rerank mode — it silently discards the <Instruct> and returns degenerate, instruction-blind scores. Use the completion + logits path documented in that repo (verified end-to-end via stock llama-server, matching this model within ~0.01 at Q8_0).

Results

WarrantBench (Hanno-Labs/warrantbench) — it out-steers a frontier LLM:

On novel rules it never trained on: 0.95 ("both mention a figure ≥ $1B") and 0.95 ("both involve a government or regulator"), vs 0.35 / 0.63 for flash-lite.

FollowIR (public instruction-following retrieval, p-MRR): Bosun-XS tops the board where most retrievers score zero or negative — they read the instruction as keywords; Bosun reads it as a rule.

Files

Links

• Launch post — Introducing Bosun
• GGUF (run locally) — Hanno-Labs/bosun-xs-GGUF
• WarrantBench — github.com/Hanno-Labs/warrantbench (dataset)

From Hanno Labs.