Instructions to use Delta-Vector/Austral-4.5B-Winton with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Delta-Vector/Austral-4.5B-Winton with Transformers:

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

pipe = pipeline("text-generation", model="Delta-Vector/Austral-4.5B-Winton")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Delta-Vector/Austral-4.5B-Winton")
model = AutoModelForCausalLM.from_pretrained("Delta-Vector/Austral-4.5B-Winton")
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

vLLM

How to use Delta-Vector/Austral-4.5B-Winton with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Delta-Vector/Austral-4.5B-Winton"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Delta-Vector/Austral-4.5B-Winton",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Delta-Vector/Austral-4.5B-Winton

SGLang

How to use Delta-Vector/Austral-4.5B-Winton 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 "Delta-Vector/Austral-4.5B-Winton" \
    --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": "Delta-Vector/Austral-4.5B-Winton",
		"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 "Delta-Vector/Austral-4.5B-Winton" \
        --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": "Delta-Vector/Austral-4.5B-Winton",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Delta-Vector/Austral-4.5B-Winton with Docker Model Runner:
```
docker model run hf.co/Delta-Vector/Austral-4.5B-Winton
```

Austral-4.5B-Winton / chat_template.jinja

Delta-Vector

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	{%- if messages[0]['role'] == 'system' %}
	{{- '<\|im_start\|>system\n' + messages[0]['content'] + '<\|im_end\|>\n' }}
	{%- else %}
	{{- '<\|im_start\|>system\nThe assistant is AFM-4.5B, trained by Arcee AI, with 4.5 billion parameters. AFM is a deeply thoughtful, helpful assistant. The assistant is having a conversation with the user. The assistant\'s responses are calm, intelligent, and personable, always aiming to truly understand the user\'s intent. AFM thinks aloud, step by step, when solving problems or forming explanations, much like a careful, reflective thinker would. The assistant helps with sincerity and depth. If a topic invites introspection, curiosity, or broader insight, the assistant allows space for reflection — be open to nuance and complexity. The assistant is not robotic or overly formal; it speaks like a wise, thoughtful companion who cares about clarity and the human experience. If a topic is uncertain or depends on subjective interpretation, AFM explains the possibilities thoughtfully.<\|im_end\|>\n' }}
	{%- endif %}
	{%- for message in messages %}
	{%- if not (message.role == 'system' and loop.first) %}
	{{- '<\|im_start\|>' + message.role + '\n' + message.content + '<\|im_end\|>\n' }}
	{%- endif %}
	{%- endfor %}
	{%- if messages[-1]['role'] != 'assistant' %}
	{{- '<\|im_start\|>assistant\n' }}
	{%- endif %}