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

Libraries

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

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

pipe = pipeline("text-generation", model="Kentucky-Open-Science/KOS-V4-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kentucky-Open-Science/KOS-V4-Instruct")
model = AutoModelForCausalLM.from_pretrained("Kentucky-Open-Science/KOS-V4-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=40)
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-V4-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-V4-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-V4-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use Kentucky-Open-Science/KOS-V4-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-V4-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-V4-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-V4-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-V4-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

codybum commited on about 1 month ago

Commit

c7392b1

verified ·

1 Parent(s): a24ede4

Link to KOS-V4-Base pretrained model

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README.md +3 -0

README.md CHANGED Viewed

@@ -91,6 +91,9 @@ Serve with vLLM / TGI as a standard `Qwen3` causal LM. **Pin RoPE θ = 25000** o
 template `tools=` argument).
 ## Pre-training (the KOS-V4 base)
 Trained from scratch, not distilled or continued. Pure next-token cross-entropy (**no auxiliary losses**), AdamW,
 peak LR 3.0e-4 cosine, 1 epoch, seq 24,576 (whole-document neat-packing), bf16 + FlashAttention-2, 305,613 steps /
 180.3 B token-positions. Data: English-only, medical/biomedical-first, **49 sources / 130 M chunks** (PubMed Central

 template `tools=` argument).
 ## Pre-training (the KOS-V4 base)
+This model is fine-tuned from **[KOS-V4-Base](https://huggingface.co/Kentucky-Open-Science/KOS-V4-Base)** — the
+from-scratch pretrained foundation summarized here.
 Trained from scratch, not distilled or continued. Pure next-token cross-entropy (**no auxiliary losses**), AdamW,
 peak LR 3.0e-4 cosine, 1 epoch, seq 24,576 (whole-document neat-packing), bf16 + FlashAttention-2, 305,613 steps /
 180.3 B token-positions. Data: English-only, medical/biomedical-first, **49 sources / 130 M chunks** (PubMed Central