Instructions to use Kentucky-Open-Science/KOS-V4-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kentucky-Open-Science/KOS-V4-Base with Transformers:

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

pipe = pipeline("text-generation", model="Kentucky-Open-Science/KOS-V4-Base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kentucky-Open-Science/KOS-V4-Base")
model = AutoModelForCausalLM.from_pretrained("Kentucky-Open-Science/KOS-V4-Base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kentucky-Open-Science/KOS-V4-Base 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-V4-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kentucky-Open-Science/KOS-V4-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Kentucky-Open-Science/KOS-V4-Base

SGLang

How to use Kentucky-Open-Science/KOS-V4-Base 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-V4-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kentucky-Open-Science/KOS-V4-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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-V4-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kentucky-Open-Science/KOS-V4-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Kentucky-Open-Science/KOS-V4-Base with Docker Model Runner:
```
docker model run hf.co/Kentucky-Open-Science/KOS-V4-Base
```

codybum commited on Jul 20

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@@ -123,8 +123,7 @@ Token counts are char-estimates against the packed cache.
 | Hindawi OA journals | 2.93 B | | s2orc | 1.21 B |
 | MeDAL (PubMed abstracts) | 2.49 B | | | |
-**+ ~30 smaller sources:** clinical narratives (open_patients, mimic-iv-ed, mimic-cxr, ctrate, coral, tcga_reports,
-mts_dialog), knowledge / guidelines (stackexchange-science 0.27 B, dailymed, cpg, gene_ontology, medlineplus,
 orphapacket, medmentions, trialgpt), pharmacovigilance / relational rendered to NL (ctd, faers, aeolus, onsides,
 sider, cdc_places, cbioportal, civic, ade_corpus_v2), and deliberate register-diversity (locus legal-code).
@@ -134,9 +133,6 @@ license verification; they will be added once resolved.)*
 **Disclosed issue:** ~35% of tokens are duplicates (a FineWeb-Edu sharding build bug + PMC repetition); a deduped
 corpus (`v4_dedup`, 79.4 M docs, 0% dup) is ready but was **not** trained — this release is the original single-epoch corpus.
-**Governance:** includes PhysioNet-credentialed, de-identified, redistribution-restricted clinical sources (MIMIC-IV
-discharge / radiology, MIMIC-CXR) — any downstream use must confirm PhysioNet DUA compliance.
 ## Evaluation — 19-test suite, 75 metrics, vs a 16-model fleet
 KOS-V4-Base is benchmarked as a **from-scratch, single-epoch base against 16 external models trained on 1.7–200× more
 data** (0.3–36 T tokens). Pool of 17 models, 95 ranked metrics. BPB (bits-per-byte) is tokenizer-agnostic and included

 | Hindawi OA journals | 2.93 B | | s2orc | 1.21 B |
 | MeDAL (PubMed abstracts) | 2.49 B | | | |
+**+ ~30 smaller sources:** open clinical narratives, knowledge / guidelines (stackexchange-science 0.27 B, dailymed, cpg, gene_ontology, medlineplus,
 orphapacket, medmentions, trialgpt), pharmacovigilance / relational rendered to NL (ctd, faers, aeolus, onsides,
 sider, cdc_places, cbioportal, civic, ade_corpus_v2), and deliberate register-diversity (locus legal-code).
 **Disclosed issue:** ~35% of tokens are duplicates (a FineWeb-Edu sharding build bug + PMC repetition); a deduped
 corpus (`v4_dedup`, 79.4 M docs, 0% dup) is ready but was **not** trained — this release is the original single-epoch corpus.
 ## Evaluation — 19-test suite, 75 metrics, vs a 16-model fleet
 KOS-V4-Base is benchmarked as a **from-scratch, single-epoch base against 16 external models trained on 1.7–200× more
 data** (0.3–36 T tokens). Pool of 17 models, 95 ranked metrics. BPB (bits-per-byte) is tokenizer-agnostic and included