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)

# pip install -U transformers accelerate
# 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=256)
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
```

Bc-AI commited on 18 days ago

Commit

ebbd6b4

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Update README.md

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README.md +42 -8

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@@ -8,15 +8,49 @@ datasets:
 tags:
 - full-fine-tune
 - sft
 - qwen3.5
 ---
-# Qwen3.5-4B — Full SFT
-- **Base model:** `Qwen/Qwen3.5-4B`
-- **Dataset:** `Bc-AI/SFT-Ultra`
-- **Training type:** Full parameter supervised fine-tuning (no LoRA)
-- **Max sequence length:** 2048
-- **Max steps:** 2500
-- **Learning rate:** 1e-05
-- **Precision:** bf16

 tags:
 - full-fine-tune
 - sft
+- dpo
 - qwen3.5
+- smilyai
 ---
+# 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:
+1. **Supervised Fine-Tuning (SFT)** — trained the base model on our curated instruction and reasoning data.
+2. **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.