Instructions to use allura-org/Koto-Small-7B-PT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use allura-org/Koto-Small-7B-PT with Transformers:

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

pipe = pipeline("text-generation", model="allura-org/Koto-Small-7B-PT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("allura-org/Koto-Small-7B-PT")
model = AutoModelForMultimodalLM.from_pretrained("allura-org/Koto-Small-7B-PT")
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 allura-org/Koto-Small-7B-PT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allura-org/Koto-Small-7B-PT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allura-org/Koto-Small-7B-PT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/allura-org/Koto-Small-7B-PT

SGLang

How to use allura-org/Koto-Small-7B-PT 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 "allura-org/Koto-Small-7B-PT" \
    --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": "allura-org/Koto-Small-7B-PT",
		"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 "allura-org/Koto-Small-7B-PT" \
        --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": "allura-org/Koto-Small-7B-PT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use allura-org/Koto-Small-7B-PT with Docker Model Runner:
```
docker model run hf.co/allura-org/Koto-Small-7B-PT
```

Fizzarolli commited on Aug 31, 2025

Commit

a6a181c

verified ·

1 Parent(s): 84419e3

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -22,7 +22,7 @@ This model is not intended for use outside of raw text completion settings, such
 It was trained at 32k, but as not all samples were this long, we expect that in the best case you can get ~16k effective context.
-We found that 1.5-1.55 temperature and 0.05-0.1 min_p worked best, but YMMV!
 ## Datasets

 It was trained at 32k, but as not all samples were this long, we expect that in the best case you can get ~16k effective context.
+We found that 1.25 temperature and 0.05 min_p worked best, but YMMV!
 ## Datasets