KOS-V5-Retriever · "Catbird"

Developed by

University of Kentucky

• College of Medicine, Office for Research
• Center for Clinical and Translational Science (CCTS)

University of Louisville

• Kentucky Center for Digital Innovation

🔎 This repository is the retrieval / embedding adapter for KOS-V5-Instruct. It is a ~264 MB LoRA adapter (PEFT, rank 32), not a standalone model. Loaded on top of the unmodified KOS-V5-Instruct weights (verified byte-identical — model.safetensors sha256 matches the published base), it turns the generator into a dense text-retrieval embedding model: same medical foundation, re-purposed to produce a vector per input for semantic search and RAG. Detach the adapter and you have the original generator back.

KOS-V5 (codename Catbird) is the fifth-generation Kentucky Open Science model line — a 3.72B-parameter medical language model trained from scratch (not distilled, not pruned, not continued-pretrained from a general base). This adapter shares that foundation; the sections below cover (1) what the adapter does and how well it retrieves, then (2) the base model it is built on.

⚠️ Research use only. Provided for research purposes only; not for commercial, clinical, legal, or production-grade use. The user assumes all risks.

🔒 Private research artifact. This repository is private and is not a public release.

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1 · Retrieval & embeddings — what this adapter does

The base is a decoder LLM (it generates text left-to-right). This adapter converts it into a text encoder (llm2vec-style) with three inference-time changes plus a small trained delta:

1. Bidirectional attention — the causal mask is replaced with a padding-only mask, so every token attends to the whole sequence.
2. Mean pooling — the sequence embedding is the attention-masked mean of the last hidden states, L2-normalized.
3. LoRA (rank 32) on all seven linear projections (q,k,v,o,gate,up,down), contrastively trained so related (query, passage) pairs land close and unrelated pairs far apart. The base weights are frozen; only the LoRA delta is learned.

The adapter does not generate text and does not alter the base's chat/tool behaviour — it is a separate, hot-swappable head for retrieval only.

Results — official BEIR (SciFact)

Scored with the official beir library (GenericDataLoader + EvaluateRetrieval + pytrec_eval — the exact scorer behind the public BEIR/MTEB leaderboard), zero-shot: the training data explicitly excludes SciFact, verified clean (0 of 300 SciFact test queries appear in training).

BEIR SciFact (NDCG@10, official)	value
KOS-V5-Retriever (this adapter)	0.7007
Recall@10	0.8639
BM25 (reference)	0.665
strong dense retrievers — GTR / E5 / BGE (reference)	0.70–0.76

A legitimately strong dense retriever — it beats BM25 and sits in the strong-dense band, zero-shot, on a base a fraction the size of typical dense-retrieval models.

Scope / honesty. This is one BEIR task (SciFact). It has not yet been evaluated on other BEIR tasks or MTEB; read 0.70 as a strong single-benchmark result, not a full retrieval profile. Broader evaluation (nfcorpus, fiqa, MTEB-medical) is planned. Contamination on other benchmarks is unchecked; BEIR SciFact is zero-shot CLEAN.

Usage

All four moving parts (base + LoRA + bidirectional patch + mean-pool) must be applied together, or the number above will not reproduce. This snippet is the evaluation encoder.

import torch, torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer
from peft import PeftModel

BASE    = "Kentucky-Open-Science/KOS-V5-Instruct"
ADAPTER = "Kentucky-Open-Science/KOS-V5-Retriever"   # this repo

# 1) bidirectional attention: replace Qwen3's causal mask with a padding-only mask
import transformers.models.qwen3.modeling_qwen3 as Q3
def _bidirectional(*args, **kwargs):
    ie = kwargs.get("input_embeds", args[1] if len(args) > 1 else None)
    am = kwargs.get("attention_mask", args[2] if len(args) > 2 else None)
    if am is None:
        return None
    dt = ie.dtype if ie is not None else torch.float32
    return (1.0 - am[:, None, None, :].to(dt)) * torch.finfo(dt).min
Q3.create_causal_mask = _bidirectional

# 2) base (frozen) + LoRA adapter
tok   = AutoTokenizer.from_pretrained(ADAPTER)
base  = AutoModel.from_pretrained(BASE, torch_dtype=torch.float32)
model = PeftModel.from_pretrained(base, ADAPTER).cuda().eval()

# 3) mean-pool + L2-normalize
@torch.no_grad()
def encode(texts, max_length=256):
    b = tok(texts, padding=True, truncation=True, max_length=max_length, return_tensors="pt").to("cuda")
    h = model(input_ids=b["input_ids"], attention_mask=b["attention_mask"]).last_hidden_state
    am = b["attention_mask"].unsqueeze(-1).float()
    return F.normalize((h * am).sum(1) / am.sum(1).clamp(min=1), dim=1)

docs    = encode(["Vitamin D deficiency is associated with increased risk of respiratory infection."])
queries = encode(["Does low vitamin D raise infection risk?"])
print(queries @ docs.T)   # cosine similarity; rank candidates by this

Retrieval is symmetric — use the same encode for queries and documents. The adapter's meta.json records the required config (bidirectional: true, pooling: mean).

Training (this adapter)

• Base: Kentucky-Open-Science/KOS-V5-Instruct (frozen; byte-identical to the published weights).
• Adapter: PEFT LoRA rank 32, targets q,k,v,o,gate,up,down, bf16.
• Objective: contrastive (in-batch + hard negatives) over public (query, positive, negative) retrieval pairs, with SciFact excluded so the evaluation is zero-shot. No dataset content is distributed with this adapter.

Retrieval limitations

• Retrieval only. Produces embeddings; it does not generate. For chat / instruction following / tool calling, use the base model without the adapter.
• Single-benchmark evidence (BEIR SciFact). Generalization is unproven.
• Requires the exact inference recipe (bidirectional patch + mean-pool). A plain causal PeftModel load will not reproduce the results.

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2 · The base model — KOS-V5-Instruct

The rest of this card describes the model this adapter is built on. These are the base generator's numbers (instruction following, tool calling, medical QA); the adapter's own metric is the BEIR retrieval number above.

A 3.72B-parameter medical language model trained from scratch. KOS-V5 (codename Catbird) holds the instruction-tuned head of the line: the KOS-V5-Base pretraining checkpoint taken through SFT and two GRPO reinforcement-learning legs. Unlike the base, it follows instructions and calls tools.

Code name: Catbird. Native to Kentucky, the Gray Catbird is a songbird famous for its cat-like "meow"; trained from scratch by teams from the University of Kentucky (Cat) and University of Louisville (Bird).

Core specifications

Attribute	Detail
Architecture	Decoder-only Transformer (Qwen3ForCausalLM), Grouped-Query Attention
Parameters	3.715 B
Hidden / Layers	2560 / 36
Attention	32 query / 8 KV heads (GQA 4:1), head_dim 128, per-head QK-RMSNorm
Feed-forward	SwiGLU, intermediate 9728
Vocabulary	32,000, custom medical byte-level BPE
Context length	32,768
Position encoding	RoPE, θ = 25,000
Embeddings	tied
Precision	bfloat16 (7.43 GB, single shard)

The medical foundation

This is a medical model. It inherits a base trained on a 54-source medical/biomedical corpus. The strongest evidence is bits-per-byte on held-out medical text (tokenizer-agnostic). In a 17-model pool — including BioMedLM, Meditron-7B, PMC-LLaMA-7B and MedGemma-4B — the KOS-V5 base ranks 1 of 17:

medical text (BPB, lower is better)	KOS-V5-Base	rank
5-corpus mean, held-out medical text	0.4635	1 / 17
clinical narratives	0.4179	1 / 17
radiology	0.5132	1 / 17
chest X-ray reports	0.6688	1 / 17
BIOSSES biomedical sentence similarity (Pearson / Spearman)	0.7097 / 0.7014	1 / 17

Every comparator was trained on 1.3–153× more data. See KOS-V5-Base for the full 96-metric evaluation.

Base generation benchmarks (official suites)

EleutherAI lm-evaluation-harness 0.4.12.dev0 (commit c1c4bea), pristine clone, stock tasks; BFCL via the official bfcl_eval (FC mode, non-live AST) on vLLM.

benchmark	KOS-V5-Instruct	KOS-V4-Instruct
IFEval strict-avg	72.19	61.6
MMLU (57-subj, 5-shot, acc)	0.4512	0.2782
medical-9 MMLU mean	0.4915	0.2752
PubMedQA / MedQA / MedMCQA	0.706 / 0.380 / 0.365	0.686 / 0.282 / 0.278
BFCL simple / multiple / parallel	85.0 / 84.0 / 80.5	72.8 / 73.0 / 60.5
RGB negative-rejection (official)	57.33	—

IFEval is reported as strict-avg = (prompt-strict + inst-strict) / 2. On instruction following the base places first among nine university-built instruct models and above the original GPT-3.5-turbo generation; its BFCL tool-calling is above the Qwen3-4B-Instruct-2507 peer. The peer still leads on parametric knowledge (MMLU 0.7266) and raw IFEval (84.71). Forgetting control: OOD broad-holdout perplexity at 0.99× the pre-RL base (no measurable forgetting).

⚠️ Read the medical signal from BPB, not the MCQ scores. KOS models place little probability mass on MCQ answer letters; the format, not the knowledge, is the bottleneck. A model ranking 1 of 17 at modelling clinical text while scoring modestly on multiple-choice is exhibiting exactly that gap.

Data contamination (base)

• IFEval: CLEAN (verbatim) — 0 exact containments vs the 541 official test prompts.
• BFCL: CLEAN (verbatim) — 0 exact containments vs 5,437 official prompts.
• MMLU / PubMedQA / MedQA / MedMCQA: UNCHECKED.
• Retrieval (this adapter), BEIR SciFact: CLEAN (zero-shot) — see §1.

Related models

• KOS-V5-Instruct — the base this adapter attaches to (3.72B, medical, instruction + tool-calling).
• KOS-V5-Base — the from-scratch pretrained foundation (235.2B tokens).
• KOS-V4-Instruct — previous generation.

Intended use & limitations

Research use only. English only. Not for clinical, commercial, legal, or production-grade use. Retrieval outputs and any downstream results may be wrong or misleading; this model must not be used to make or inform medical decisions.

Naming

The program is KOS (KOS-V1..V6); the V5 series codename is Catbird. Earlier internal names are not used.