Instructions to use Smilyai-labs/Mira-1-XL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Smilyai-labs/Mira-1-XL with Transformers:

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

pipe = pipeline("image-text-to-text", model="Smilyai-labs/Mira-1-XL")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Smilyai-labs/Mira-1-XL")
model = AutoModelForMultimodalLM.from_pretrained("Smilyai-labs/Mira-1-XL", 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 = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Smilyai-labs/Mira-1-XL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Smilyai-labs/Mira-1-XL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Smilyai-labs/Mira-1-XL",
		"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/Smilyai-labs/Mira-1-XL

SGLang

How to use Smilyai-labs/Mira-1-XL 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 "Smilyai-labs/Mira-1-XL" \
    --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": "Smilyai-labs/Mira-1-XL",
		"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 "Smilyai-labs/Mira-1-XL" \
        --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": "Smilyai-labs/Mira-1-XL",
		"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 Smilyai-labs/Mira-1-XL with Docker Model Runner:
```
docker model run hf.co/Smilyai-labs/Mira-1-XL
```

mira-1-xl

mira-1-xl is a multimodal (vision + text) 27B-class model merge built on Qwen3.5-27B, blended with Claude-Opus-style reasoning distills and a Fable-style creative distill.

This repo is intended as a premium “daily driver”:

strong instruction following
improved reasoning / “thinking” behavior vs base
better creative tone + narrative output
keeps Qwen3.5’s vision capability (image+text conversations)

Not affiliated with Alibaba/Qwen, TeichAI, Jackrong, or Anthropic/Claude. “Claude / Opus / Fable” are referenced only to describe the upstream community models used as merge sources.

What’s inside

Architecture: Qwen3.5 multimodal (Qwen3_5ForConditionalGeneration)
Precision: BF16 weights
Modalities: image + text → text

Merge recipe (DARE-TIES)

This model is a 3-way DARE-TIES merge (drop + rescale, then weighted delta sum), with per-source sparsity (“density”) and weights.

Important: DARE-style merges are stochastic unless you fix RNG seeds. Your merge may differ slightly if you re-run.

Base

Qwen/Qwen3.5-27B

Merge sources + parameters

Source model	Role / flavor	density	weight
`Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled`	reasoning + structured solutions	0.70	0.40
`TeichAI/Qwen3.5-27B-Claude-Opus-4.6-Distill`	instruction following + general ability	0.65	0.35
`TeichAI/Qwen3.8-27B-Fable-Distill`	creative writing / planning style	0.60	0.30

Normalization: enabled (per-tensor norm stabilization)
Output dtype: bf16

Intended use

Great for:

multimodal Q&A (“what’s in this image?”, “summarize this screenshot”, etc.)
long-form writing (stories, scripts, outlines)
agent-style planning (multi-step task decomposition)
general chat + instruction following
“reasoning-ish” tasks where base Qwen can be too shallow

Not ideal for:

safety-critical domains (medical/legal/financial advice) without human review
guaranteed factuality (hallucinations still happen)
strict reproducibility (DARE merges are not deterministic unless seeded)

Usage (Transformers)

1) Quick pipeline (image-text-to-text)

from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Bc-AI/mira-1-xl")  # <- change to your repo id

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? Answer in one sentence."}
    ]
  }
]

out = pipe(text=messages, max_new_tokens=128)
print(out)