Instructions to use beyoru/mira-12b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beyoru/mira-12b with Transformers:

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

pipe = pipeline("text-generation", model="beyoru/mira-12b")
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("beyoru/mira-12b")
model = AutoModelForMultimodalLM.from_pretrained("beyoru/mira-12b", 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 beyoru/mira-12b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "beyoru/mira-12b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beyoru/mira-12b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/beyoru/mira-12b

SGLang

How to use beyoru/mira-12b 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 "beyoru/mira-12b" \
    --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": "beyoru/mira-12b",
		"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 "beyoru/mira-12b" \
        --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": "beyoru/mira-12b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use beyoru/mira-12b with Docker Model Runner:
```
docker model run hf.co/beyoru/mira-12b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Mira 12B

A 12B chat model that talks like a person, not like an assistant. Short when you're short, warm when you're down, straight to the point when you're busy. No bullet-point essays, no "Great question!", no em dashes. It thinks before it answers, and its thinking reads like someone mulling it over, not a to-do list.

It keeps the base model's brains: math, code and tool calling stay where they were, and it runs on a single consumer GPU or in your device

Mira 12B intro: a prompt, Gemma 4 12B's long markdown answer next to Mira's short reply, real replies in English, Japanese and Chinese, and the style and ability numbers from the Results table

Before and after

Same prompt, same sampling. Held-out prompts the model never saw, outputs unedited.

English: is it fine to dislike a movie everyone calls a classic. Mira answers in a few honest sentences; Gemma 4 12B writes a long breakdown with headings

Japanese: worried a dating-app match might be married. Mira tells him searching won't work and to just ask her; Gemma 4 12B lists numbered points

Traditional Chinese: someone lonely at 2:30am. Mira answers in two short lines; Gemma 4 12B writes many paragraphs

English: a playful order to make five slides. Mira plays along and lists the five slides; Gemma 4 12B writes a role-play monologue

It writes for you

Rewriting your own message so it sounds like you, a poem for someone you love, a lullaby for a baby who won't sleep, a text you're too tired to word: Mira gives you one good version you can use right away, instead of three options and a lecture.

Japanese: rewrite a stiff LINE message to a landlord so it sounds natural and asks about the 1,500 yen fee. Mira returns one ready-to-send message; Gemma 4 12B offers three patterns with headings

English: a short poem for a mother's 80th birthday, warm but not a greeting card. Mira writes seven lines; Gemma 4 12B writes three options with headings

Simplified Chinese: a rhyming lullaby for an eight-month-old nicknamed Tank. Mira writes three short verses; Gemma 4 12B writes three versions with bold text and notes

English: a quick congrats text for a friend who finished his first Ironman. Mira writes two short texts; Gemma 4 12B writes a list of options

Results

Mira 12B vs Gemma 4 12B: far less markdown, dashes and assistant-style thinking, no stuck repetition, and higher GSM8K, code and tool-calling scores

Held-out prompts the model never saw, 3 samples each, default sampling.

	Mira 12B	Gemma 4 12B
Answers with markdown in casual chat	2.6%	87.7%
Answers with em/en dashes	0.3%	34.5%
Thinking that reads like an assistant plan ("the user wants…")	4.4%	76.3%
Replies in the user's language	100%	100%
Answers that get stuck repeating themselves	0%	0.6%

Ability kept (greedy): GSM8K 94.0% (base 70.0%, mostly cut off mid-thought), code with unit tests 84.2% (base 57.9%), tool calling 62.3% (base 59.2%).

Usage

Sampling is already set in generation_config.json (temperature 1.0, top_p 0.95, top_k 64), so you don't need to pass anything. It also holds up at lower temperatures.

vllm serve beyoru/mira-12b --reasoning-parser gemma4 --tool-call-parser gemma4 --enable-auto-tool-choice

from transformers import AutoTokenizer, AutoModelForCausalLM
tok = AutoTokenizer.from_pretrained("beyoru/mira-12b")
model = AutoModelForCausalLM.from_pretrained("beyoru/mira-12b", torch_dtype="bfloat16", device_map="auto")
msgs = [{"role": "user", "content": "long day. my brain is soup"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, enable_thinking=True, return_tensors="pt").to(model.device)
print(tok.decode(model.generate(ids, max_new_tokens=1024, do_sample=True)[0][ids.shape[1]:]))

Limitations

It can make up specifics when it doesn't know them (song titles, names, rules), so check facts before you rely on them. In about 1% of answers it closes its thinking twice and writes a second version after a stray <channel|>; keeping only the text after the last <channel|> fixes it. Style numbers are automatic counts, not human ratings.

License

Non-commercial use only. The Mira 12B weights, this card and its images are released under CC BY-NC 4.0: you can use, share and adapt the model for research and personal projects with attribution, but you may not use it, or offer it or its outputs as part of a paid product or service, without written permission. For commercial licensing, contact beyoru.

Mira 12B is built on Gemma 4 12B by Google, licensed under the Apache License 2.0; see NOTICE and LICENSE-APACHE-2.0.

Citation

@misc{mira12b2026,
  title        = {Mira 12B: a chat model that talks like a person},
  author       = {beyoru},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/beyoru/mira-12b}},
  note         = {Fine-tuned from Gemma 4 12B}
}

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Safetensors

Model size

12B params

Tensor type

BF16

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Quantizations

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