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

Libraries

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

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

pipe = pipeline("text-generation", model="Kentucky-Open-Science/KOS-V5-Base")

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

tokenizer = AutoTokenizer.from_pretrained("Kentucky-Open-Science/KOS-V5-Base")
model = AutoModelForCausalLM.from_pretrained("Kentucky-Open-Science/KOS-V5-Base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kentucky-Open-Science/KOS-V5-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-V5-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-V5-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use Kentucky-Open-Science/KOS-V5-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-V5-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-V5-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-V5-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-V5-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

codybum commited on Jul 13

Commit

224f9dc

verified ·

1 Parent(s): b037707

Model card: UK CoM/CCTS attribution in structured metadata; win/loss by quartile; comparator roster; drop step count

Browse files

Files changed (1) hide show

README.md +9 -0

README.md CHANGED Viewed

@@ -52,6 +52,15 @@ This is a **base model**. It has had no instruction tuning, no RLHF, and no chat
 completes text; it does not follow instructions. It is the initialisation for a downstream SFT/RL
 line, and it is released as a research artifact.
 ---
 ## At a glance

 completes text; it does not follow instructions. It is the initialisation for a downstream SFT/RL
 line, and it is released as a research artifact.
+**Code name: Catbird.** The KOS-V5 series is nicknamed Catbird, a native bird 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 applications. The user assumes all risks
+> associated with its use.
 ---
 ## At a glance