Instructions to use Bc-AI/T1-Mini-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bc-AI/T1-Mini-Preview with Transformers:

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

pipe = pipeline("image-text-to-text", model="Bc-AI/T1-Mini-Preview")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Bc-AI/T1-Mini-Preview")
model = AutoModelForCausalLM.from_pretrained("Bc-AI/T1-Mini-Preview", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
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 Bc-AI/T1-Mini-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Bc-AI/T1-Mini-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bc-AI/T1-Mini-Preview",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Bc-AI/T1-Mini-Preview

SGLang

How to use Bc-AI/T1-Mini-Preview 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 "Bc-AI/T1-Mini-Preview" \
    --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": "Bc-AI/T1-Mini-Preview",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "Bc-AI/T1-Mini-Preview" \
        --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": "Bc-AI/T1-Mini-Preview",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Bc-AI/T1-Mini-Preview with Docker Model Runner:
```
docker model run hf.co/Bc-AI/T1-Mini-Preview
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SmilyAI Labs T1-Mini-Preview

T1-Mini-Preview is an early preview of the upcoming T1-Mini model from SmilyAI Labs.

T1-Mini-Preview is based on Qwen/Qwen3.5-4B and was fully fine-tuned on Bc-AI/SFT-Ultra, a curated instruction-tuning dataset prepared for SmilyAI's small-model research.

Training

The model was trained in two main stages:

Supervised Fine-Tuning (SFT) — trained the base model on our curated instruction and reasoning data.
Direct Preference Optimization (DPO) — further refined the model's responses using preference-based training.

This two-stage pipeline was designed to improve instruction following, response quality, and overall conversational behavior while keeping the model relatively small and efficient.

Model Status

T1-Mini-Preview is a preview release, not the final T1-Mini model. Training, evaluation, and further refinement are still ongoing.

We are releasing this version so the community can experiment with it and provide feedback while development continues.

Base Model

Base: Qwen/Qwen3.5-4B
Training: Full fine-tuning
Primary language: English
Fine-tuning dataset: Bc-AI/SFT-Ultra
Training stages: SFT → DPO
License: Apache 2.0

About SmilyAI Labs

SmilyAI Labs is a small open-source AI project focused on building capable, efficient, and accessible AI models.

We're experimenting with smaller models that can deliver strong performance without requiring enormous amounts of compute.

🚀 T1-Mini-Preview is one step toward that goal.

Disclaimer

This is an experimental preview model. Its behavior and capabilities may differ from the final T1-Mini release, and it may occasionally produce incorrect, inconsistent, or undesirable outputs.

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for Bc-AI/T1-Mini-Preview

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Finetuned

(733)

this model

Quantizations

1 model

Bc-AI
/

T1-Mini-Preview