Instructions to use Kentucky-Open-Science/KOS-V5-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kentucky-Open-Science/KOS-V5-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Kentucky-Open-Science/KOS-V5-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kentucky-Open-Science/KOS-V5-Instruct")
model = AutoModelForCausalLM.from_pretrained("Kentucky-Open-Science/KOS-V5-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kentucky-Open-Science/KOS-V5-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kentucky-Open-Science/KOS-V5-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kentucky-Open-Science/KOS-V5-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kentucky-Open-Science/KOS-V5-Instruct

SGLang

How to use Kentucky-Open-Science/KOS-V5-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Kentucky-Open-Science/KOS-V5-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kentucky-Open-Science/KOS-V5-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Kentucky-Open-Science/KOS-V5-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kentucky-Open-Science/KOS-V5-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kentucky-Open-Science/KOS-V5-Instruct with Docker Model Runner:
```
docker model run hf.co/Kentucky-Open-Science/KOS-V5-Instruct
```

KOS-V5-Instruct / README.md

codybum

Update README.md

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	language:
	- en
	developed_by: University of Kentucky (College of Medicine Office for Research; Center for Clinical and Translational Science) and University of Louisville (Kentucky Center for Digital Innovation)
	affiliations:
	- name: University of Kentucky, College of Medicine Office for Research
	url: https://medicine.uky.edu/sites/research
	- name: University of Kentucky, Center for Clinical and Translational Science (CCTS)
	url: https://www.ccts.uky.edu/
	- name: University of Louisville, Kentucky Center for Digital Innovation
	url: https://centers.louisville.edu/kentucky-center-digital-innovation
	model_name: KOS-V5-Instruct
	model_codename: Catbird
	model_type: qwen3
	base_model: Kentucky-Open-Science/KOS-V5-Base
	tags:
	- medical
	- clinical
	- biomedical
	- instruction-following
	- tool-calling
	- function-calling
	- from-scratch
	- qwen3
	- KOS-V5
	- university-of-kentucky
	- university-of-louisville
	- ccts
	---

	<p align="center">
	<img src="catbird_llm_logo.png" alt="Catbird" width="320"/>
	</p>

	# KOS-V5-Instruct · "Catbird"

	Developed by

	University of Kentucky
	- [College of Medicine, Office for Research](https://medicine.uky.edu/sites/research)
	- [Center for Clinical and Translational Science (CCTS)](https://www.ccts.uky.edu/)

	University of Louisville
	- [Kentucky Center for Digital Innovation](https://centers.louisville.edu/kentucky-center-digital-innovation)

	A 3.72B-parameter medical language model trained from scratch. It is not distilled, not pruned and not
	continued-pretrained from a general base. KOS-V5 (codename Catbird) is the fifth-generation Kentucky Open
	Science model line. This repository holds the instruction-tuned head of that line: the
	[KOS-V5-Base](https://huggingface.co/Kentucky-Open-Science/KOS-V5-Base) pretraining checkpoint, taken through
	SFT and two GRPO reinforcement-learning legs.

	Unlike the base, this model follows instructions and calls tools. It is the downstream SFT/RL artifact that
	[KOS-V5-Base](https://huggingface.co/Kentucky-Open-Science/KOS-V5-Base) was built to initialise.

	Code name: Catbird. The KOS-V5 series is nicknamed Catbird; native to Kentucky, the Gray Catbird
	(Dumetella carolinensis) is a medium-sized songbird famous for its distinct, cat-like "meow" call. This LLM
	was trained completely from scratch by teams from the University of Kentucky (Cat) and University of Louisville
	(Bird), so the code name is fitting.

	> ⚠️ Research use only. This model is provided for research purposes only and must not be used for any
	> commercial, clinical, legal, or production-grade application. The user assumes all risks associated with its use.

	---

	Its instruction ability comes from GRPO reinforcement learning against the official IFEval verifier, and its
	tool-calling ability from a second GRPO leg against the official BFCL AST checker, on a base that ranks
	first of 17 at modelling held-out clinical text.

	IFEval reported as strict-avg = `(prompt-level strict + instruction-level strict) / 2` — the exact metric the
	Hugging Face Open LLM Leaderboard publishes as "IFEval."

	\| IFEval strict-avg \| model \| who built it, and how \|
	\| --: \| :-- \| :-- \|
	\| 72.19 \| KOS-V5-Instruct (ours) \| University research team, 235B tokens, from scratch \|
	\| 64.7 \| Qwen2.5-3B-Instruct \| Alibaba, ~18 trillion tokens \|
	\| 61.6 \| [KOS-V4-Instruct](https://huggingface.co/Kentucky-Open-Science/KOS-V4-Instruct) (previous generation) \| University research team, 180B tokens, 24 GPUs \|
	\| 55.9 \| GPT-3.5-turbo-1106 (the original ChatGPT) \| OpenAI, ~10,000-GPU supercomputer \|

	KOS-V5-Instruct improves on KOS-V4-Instruct across every benchmark measured: IFEval strict-avg
	61.6 → 72.19 (+10.6), MMLU 0.2782 → 0.4512 (+17.3), medical QA (PubMedQA 0.7060, MedQA 0.3802,
	MedMCQA 0.3648 — all up on V4), and official BFCL function-calling
	72.75/73.00/60.50 → 85.00/84.00/80.50 (+12.3 / +11.0 / +20.0). It clears the original GPT-3.5-turbo
	generation and the commercially trained Qwen2.5-3B on instruction following, and its tool calling now runs
	above the Qwen3-4B-Instruct-2507 peer.

	## 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) \|

	## Pre-training (the KOS-V5 base)

	Fine-tuned from [KOS-V5-Base](https://huggingface.co/Kentucky-Open-Science/KOS-V5-Base) — the from-scratch
	pretrained foundation, a complete single-epoch run over 235.2B tokens. See that card for corpus composition and
	disclosed pretraining issues.

	## Post-training (this model)

	Three stages on top of the base — no LoRA, no distillation, no reward model, no LLM judge.

	SFT — one shuffled full-parameter pass over a 736,990-record / 1.32B-token audited instruction mix
	(ChatML). The mix was deduplicated, instruction-collision canonicalized, structurally validated, per-record
	decontaminated and BFCL-closure scanned; clinical FHIR records were dropped and tool-record system prompts
	de-welded so tool use binds to the request rather than to a fixed frame.

	RL leg 1 — instruction following (GRPO via verl) — deterministic verifiable reward. The reward is the
	official `lm_eval` IFEval instruction registry — the same checkers the benchmark scores with, not a
	re-implementation. One 8-GPU wall, 164 steps, KL 0.001 (`low_var_kl`), rollout n=16.

	RL leg 2 — tool calling (GRPO via verl) — a second leg seeded from leg 1. The reward is the official
	BFCL `ast_checker` (`bfcl_eval`). Each prompt renders its tool schemas through the model's own chat template
	(byte-exact to the official `tools=` rendering), and the prompt set is filtered to only rows the official checker
	can grade. 8-GPU wall, KL 0.001, rollout n=16; this repository ships the step-40 checkpoint, selected for the
	best BFCL / abstention balance and least policy drift. BFCL rose 76.8/71.0/69.5 → 85.0/84.0/80.5 with
	instruction following, grounded abstention and knowledge all held.

	Forgetting control — out-of-distribution broad-holdout perplexity at 0.99× the pre-RL base (8.88 vs 8.97),
	measured on a web crawl postdating the training corpus. No measurable forgetting.

	## The medical foundation

	This is a medical model. KOS-V5-Instruct inherits a base trained on a 54-source medical/biomedical corpus
	— not a general-purpose model with medical fine-tuning bolted on.

	The strongest evidence is bits-per-byte on held-out medical text, which is tokenizer-agnostic and therefore
	the only strictly fair cross-model comparison. In a 17-model pool — including dedicated biomedical
	specialists BioMedLM (300B PubMed tokens), Meditron-7B, PMC-LLaMA-7B and MedGemma-4B — the KOS-V5 base ranks
	first:

	\| 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 \|
	\| BLURB biomedical probe mean \| 0.7268 \| 2 / 17 \|

	Every comparator in that pool was trained on 1.3–153× more data (0.3–36T tokens vs our 0.235T). See
	[KOS-V5-Base](https://huggingface.co/Kentucky-Open-Science/KOS-V5-Base) for the full 96-metric evaluation.

	### Medical MMLU (from the 57-subject run above)

	The 9 medical subjects of MMLU, extracted from the same official 5-shot run:

	\| medical subject \| KOS-V5-Instruct \| KOS-V4-Instruct \|
	\| :-- \| --: \| --: \|
	\| high-school biology \| 0.5774 \| 0.2387 \|
	\| clinical knowledge \| 0.5623 \| 0.3170 \|
	\| nutrition \| 0.5359 \| 0.2843 \|
	\| college biology \| 0.5347 \| 0.2917 \|
	\| medical genetics \| 0.5100 \| 0.2700 \|
	\| anatomy \| 0.4815 \| 0.3185 \|
	\| professional medicine \| 0.4375 \| 0.2132 \|
	\| college medicine \| 0.4046 \| 0.2486 \|
	\| virology \| 0.3795 \| 0.2952 \|
	\| medical-9 mean \| 0.4915 \| 0.2752 \|

	+21.6 points over KOS-V4-Instruct, and above the model's own full-MMLU average (0.4512) — the medical
	subjects are where it is strongest.

	### Medical QA benchmarks (official suites)

	Official `medqa_4options`, `medmcqa` and `pubmedqa` from the same pristine lm-evaluation-harness,
	5-shot, loglikelihood, metric `acc`.

	\| medical benchmark \| KOS-V5-Instruct \| KOS-V4-Instruct \| YuLan-Mini-Instruct \| marin-8b-instruct \| Qwen3-4B-Instruct-2507 \|
	\| :-- \| --: \| --: \| --: \| --: \| --: \|
	\| params \| 3.7B \| 3.0B \| 2.4B \| 8.0B \| 4.0B \|
	\| PubMedQA \| 0.7060 \| 0.6860 \| 0.6960 \| 0.7500 \| 0.7720 \|
	\| MedQA (USMLE, 4-option) \| 0.3802 \| 0.2820 \| 0.3511 \| 0.4878 \| 0.6159 \|
	\| MedMCQA \| 0.3648 \| 0.2778 \| 0.3856 \| 0.4961 \| 0.5804 \|

	KOS-V5-Instruct improves on KOS-V4-Instruct on all three (+9.8 MedQA, +8.7 MedMCQA, +2.0 PubMedQA).

	PubMedQA is the standout: 0.7060, ahead of YuLan-Mini and within reach of Stanford's Marin-8B at **less than
	half the parameters. PubMedQA tests comprehension of biomedical literature** — the closest of these three to
	what the base was actually trained on. The USMLE-style exam MCQs (MedQA, MedMCQA) are where the answer-letter
	bottleneck below bites hardest.

	> ⚠️ Why the MCQ numbers understate this model. Our own measurements show KOS models place very little
	> probability mass on MCQ answer letters: the format, not the knowledge, is the bottleneck. A model that ranks
	> 1 of 17 at modelling clinical text while scoring modestly on multiple-choice is exhibiting exactly that gap.
	> Read the BPB results as the medical signal and the MCQ results as a floor, not a ceiling.

	## Evaluation & benchmarks

	Official suites only, EleutherAI lm-evaluation-harness `0.4.12.dev0` at upstream commit `c1c4bea`, run from a
	pristine clone with stock, unmodified task definitions.

	- IFEval — stock `ifeval` task, 0-shot, greedy (`do_sample=false`, `temperature=0.0`),
	task-default `max_gen_toks=1280`, `--apply_chat_template`, seed 0. Constraint checking by the harness's vendored
	Google verifier (`instructions_registry`, 25 instruction types).
	- MMLU — stock `mmlu` group, official 57 subjects / 14,042 test items, 5-shot from `dev` (`first_n`),
	loglikelihood over A–D, metric `acc` (not `acc_norm`), no chat template.
	- Medical QA — stock `medqa_4options`, `medmcqa`, `pubmedqa` tasks, 5-shot, loglikelihood, metric `acc`,
	no chat template.

	\| benchmark \| KOS-V5-Instruct \| KOS-V4-Instruct \| Δ \|
	\| :-- \| --: \| --: \| --: \|
	\| IFEval strict-avg \| 72.19 \| 61.6 \| +10.6 \|
	\| IFEval prompt-strict \| 0.6728 \| 0.5471 \| +0.126 \|
	\| IFEval inst-strict \| 0.7710 \| 0.6655 \| +0.106 \|
	\| IFEval prompt-loose \| 0.6932 \| 0.5693 \| +0.124 \|
	\| IFEval inst-loose \| 0.7878 \| 0.6882 \| +0.100 \|
	\| MMLU (57-subj, 5-shot, `acc`) \| 0.4512 \| 0.2782 \| +0.173 \|

	### Tool / function calling — official BFCL

	Measured with the official `bfcl_eval` suite in FC (function-calling) mode, non-live categories, the model
	prompted in its own native tool format and served via vLLM. Tool calling is a trained objective of this model
	— the second GRPO leg optimised the official BFCL AST checker directly.

	\| BFCL (official, FC mode, non-live AST) \| KOS-V5-Instruct \| Qwen3-4B-Instruct-2507 (peer) \| KOS-V4-Instruct \|
	\| :-- \| --: \| --: \| --: \|
	\| simple (334/400) \| 85.00 \| 83.20 \| 72.75 \|
	\| multiple (157/200) \| 84.00 \| 79.00 \| 73.00 \|
	\| parallel (147/200) \| 80.50 \| 73.50 \| 60.50 \|

	KOS-V5-Instruct is above the Qwen3-4B-Instruct-2507 peer on all three BFCL categories, and far above the
	previous KOS-V4-Instruct. This is the axis the tool-calling GRPO leg was built to move, and it moved.

	> Scope. These are the non-live AST categories only (`simple_python`, `multiple`, `parallel`). The
	> live, multi-turn, web-search and memory categories were not run, so no BFCL overall score is reported
	> here — the suite's aggregate column is not meaningful when most categories are unrun.

	> Engine note. These BFCL numbers come from the official `bfcl_eval` harness on a vLLM backend, whereas
	> the IFEval and MMLU figures on this card come from the HuggingFace backend of a pristine lm-evaluation-harness.
	> Both are official suites; they are not the same inference stack, and that is stated rather than blurred.

	Cross-harness reproduction. IFEval strict-avg measured 72.19 (pristine HF harness) and 72.0 (our
	RL-evaluation harness) in two independent runs — a 0.19-point agreement across two harness builds, far below
	the benchmark's own ±2.14-point standard error on 541 prompts, so they are the same measurement.

	Harness validation. The identical pipeline scored the peer mark Qwen3-4B-Instruct-2507 at 84.71 IFEval
	strict-avg on the same pristine harness, and independently reproduced KOS-V4-Instruct's MMLU to four decimal
	places (0.2782). A score of 0.0 on this pipeline would therefore be a model property, not a harness failure.

	### Grounded abstention & robustness — official RGB

	Measured on the official RGB harness (retrieval-augmented generation benchmark).

	\| RGB (official) \| KOS-V5-Instruct \| Qwen3-4B-Instruct-2507 (peer) \|
	\| :-- \| --: \| --: \|
	\| negative rejection (declines the unanswerable) \| 57.33 \| 39.0 \|
	\| noise robustness \| 64.0 \| 93.67 \|

	Grounded abstention is a genuine strength: neg-reject 57.33 vs the peer's 39.0 — this model declines to answer
	unanswerable questions far more often than it invents an answer. Noise-robustness (64.0) improved over an earlier
	revision (58.67) but remains below the 70 threshold we treat as a pass.

	### IFEval in context (strict-avg)

	\| model \| weights \| company \| params \| IFEval strict-avg \|
	\| :-- \| :-- \| :-- \| :-- \| --: \|
	\| GPT-4o-mini \| Proprietary \| OpenAI \| 8B + \| 79 \* \|
	\| Llama-3.2-3B-Instruct \| Open \| Meta \| 3.2B \| 73.9 \|
	\| KOS-V5-Instruct (ours) \| Open \| Univ. of Kentucky / Louisville \| 3.7B \| 72.19 \|
	\| Qwen2.5-3B-Instruct \| Open \| Alibaba \| 3.0B \| 64.7 \|
	\| Phi-3-medium-4k-instruct \| Open \| Microsoft \| 14.0B \| 64.2 \|
	\| Mistral-Large \| Proprietary \| Mistral AI \| 46.7B + \| 63 \* \|
	\| KOS-V4-Instruct (previous gen) \| Open \| Univ. of Kentucky \| 3.0B \| 61.6 \|
	\| Yi-1.5-9B-Chat \| Open \| 01.AI \| 8.8B \| 60.5 \|
	\| Phi-3.5-mini-instruct \| Open \| Microsoft \| 3.8B \| 57.7 \|
	\| GPT-3.5-turbo-0613 \| Proprietary \| OpenAI \| 20B + \| 57 \* \|
	\| Phi-3-mini-4k-instruct \| Open \| Microsoft \| 3.8B \| 56.1 \|
	\| GPT-3.5-turbo-1106 \| Proprietary \| OpenAI \| 20B + \| 55.9 \|
	\| Mistral-7B-Instruct-v0.2 \| Open \| Mistral AI \| 7.2B \| 55.0 \|
	\| Llama-3.1-8B-Instruct \| Open \| Meta \| 8.0B \| 44.3 \|
	\| Llama-2-13b-chat \| Open \| Meta \| 13.0B \| 39.8 \|

	*\ strict estimate** — no official IFEval strict sub-metrics published; estimated from published AVG4 or
	prompt-strict (loose metrics run ~2–4 pts above strict). \+ unofficial params.

	### Against university-built instruction models

	Measured by us on the identical pristine harness, same protocol:

	\| model \| institution \| params \| IFEval strict-avg \| MMLU \|
	\| :-- \| :-- \| --: \| --: \| --: \|
	\| KOS-V5-Instruct (ours) \| UK / UofL \| 3.7B \| 72.19 \| 0.4512 \|
	\| marin-8b-instruct \| Stanford \| 8.0B \| 70.83 \| 0.6112 \|
	\| YuLan-Mini-Instruct \| Renmin \| 2.4B \| 61.51 \| 0.5278 \|
	\| KOS-V4-Instruct (ours) \| UK \| 3.0B \| 60.63 \| 0.2782 \|
	\| LLäMmlein-7B-chat \| Würzburg \| 7.0B \| 54.07 \| 0.5252 \|
	\| Poro-34B-chat \| U Turku \| 34.2B \| 34.63 \| — \|
	\| Minerva-7B-instruct \| Sapienza \| 7.4B \| 21.51 \| 0.4071 \|
	\| CroissantLLMChat \| CentraleSupélec \| 1.3B \| 19.94 \| 0.2401 \|
	\| Tucano-2b4-Instruct \| U Bonn \| 2.4B \| 14.95 \| 0.2589 \|

	On instruction following KOS-V5-Instruct now places first among nine university-built instruct models, ahead of
	Stanford's Marin-8B (70.83) at less than half its parameters, and of Poro-34B at 9× its parameter count. Note
	that several of these models are non-English-first (Finnish, Italian, French, Portuguese, German) and are being
	measured on English benchmarks, which understates their designed capability. On parametric knowledge (MMLU) the
	larger, more heavily trained models still lead.

	## Retrieval & embeddings

	Beyond generation, KOS-V5-Instruct also serves as a dense text retriever. A companion LoRA adapter —
	[KOS-V5-Retriever](https://huggingface.co/Kentucky-Open-Science/KOS-V5-Retriever) — converts
	this model into an embedding model (llm2vec-style: bidirectional attention + mean-pooling + a contrastively-trained
	rank-32 LoRA), with these base weights frozen and unchanged.

	On the official BEIR SciFact benchmark (the `beir` library + pytrec_eval — the public-leaderboard scorer),
	zero-shot (training excluded SciFact, verified clean), it scores NDCG@10 = 0.7007 (Recall@10 0.864) — a
	strong dense retriever, above BM25 (0.665) and within the GTR/E5/BGE band (0.70–0.76). The adapter is
	hot-swappable: attach it for retrieval, detach it for generation. See the
	[adapter card](https://huggingface.co/Kentucky-Open-Science/KOS-V5-Retriever) for the encode recipe and
	the full retrieval details.

	## Known regressions and limitations

	> These are disclosed deliberately. A high benchmark score does not make this checkpoint production-ready.

	- Tool / function calling is a trained strength — and now measured against the peer. The second GRPO leg
	optimised the official BFCL AST checker directly; BFCL is 85.0/84.0/80.5, above the peer and far above the
	previous KOS generation. Earlier internal KOS tool-calling figures are deliberately omitted: several were
	measured against benchmark data the model had been trained on and are recorded in our own audit as invalid.
	- Grounded abstention / fabrication IS measured — and strong. On the official RGB harness, negative-rejection
	is 57.33 (peer 39.0). *Earlier revisions of this card stated fabrication was NOT independently measured; it now
	is.* Noise-robustness (64.0) is still below our 70 pass bar, so retrieval-noise handling remains a known gap.
	- The peer leads on knowledge and raw instruction following. IFEval 72.19 vs the peer's 84.71; MMLU 0.4512 vs
	0.7266; medical QA below the peer. This is a from-scratch 3.7B model on 235B tokens against one trained on orders
	of magnitude more data — strong for its scale, not state-of-the-art in absolute terms.
	- MMLU 0.4512 is above chance (0.25) but modest. This is not a knowledge model; it should not be used as a
	medical question-answering authority.
	- Not a medical-MCQ model. As with KOS-V4, do not benchmark or deploy it as one; read the BPB results (base
	card) as the medical signal and the MCQ results as a floor.

	## Data contamination

	- IFEval: CLEAN (verbatim). The SFT mix and the IFEval RL prompt set were exact-containment scanned against
	IFEval's official 541 test prompts — 0 exact containments. Exact matching cannot detect paraphrase or
	reformatting.
	- Tool calling (BFCL): CLEAN (verbatim). The tool-calling RL prompt pool was exact-containment scanned against
	5,437 full-length official BFCL prompts — 0 exact containments.
	- MMLU / PubMedQA / MedQA / MedMCQA: UNCHECKED. Contamination against these four has not been scanned for
	this checkpoint. Those numbers should be read with that caveat.

	## Prompt / chat format (ChatML)

	```
	<\|im_start\|>system
	You are a helpful assistant.<\|im_end\|>
	<\|im_start\|>user
	Write a haiku about Kentucky. Do not use any commas.<\|im_end\|>
	<\|im_start\|>assistant
	```

	Tool / function calling uses the model's native `<tools>` … `</tools>` schema block and `<tool_call>` … `</tool_call>`
	response format; pass your function schemas via the tokenizer's `apply_chat_template(..., tools=[...])`.

	## Quickstart

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "Kentucky-Open-Science/KOS-V5-Instruct"
	tok = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16", device_map="auto")

	messages = [{"role": "user", "content": "Write a haiku about Kentucky. Do not use any commas."}]
	inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
	out = model.generate(inputs, max_new_tokens=256, do_sample=False)
	print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
	```

	## Deployment

	\| Precision \| Approx. VRAM \| Notes \|
	\| :--- \| :--- \| :--- \|
	\| bfloat16 \| ~9 GB \| native weights (7.43 GB) + activations; a single 16 GB GPU is comfortable \|

	## Related models

	- [KOS-V5-Retriever](https://huggingface.co/Kentucky-Open-Science/KOS-V5-Retriever) — a LoRA
	retrieval/embedding adapter for this model (llm2vec; official BEIR SciFact NDCG@10 0.70, zero-shot).
	- [KOS-V5-Base](https://huggingface.co/Kentucky-Open-Science/KOS-V5-Base) — the from-scratch pretrained
	foundation this model is tuned from (3.72B, 235.2B tokens).
	- [KOS-V4-Instruct](https://huggingface.co/Kentucky-Open-Science/KOS-V4-Instruct) — previous generation
	(3.0B, IFEval 61.6), the public release.
	- [KOS-V4-Base](https://huggingface.co/Kentucky-Open-Science/KOS-V4-Base) — previous-generation foundation
	(3.015B, 180.3B tokens).

	## Intended use & limitations

	Research use only. English only. Not for clinical, commercial, legal, or production-grade use. Outputs may be
	factually wrong or fabricated. This model must not be used to make or inform medical decisions.

	## Naming

	The program is KOS (KOS-V1..V6). Earlier internal names are not used.