Instructions to use Lambent/Mira-v1.26.1-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lambent/Mira-v1.26.1-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Lambent/Mira-v1.26.1-27B")
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 AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Lambent/Mira-v1.26.1-27B")
model = AutoModelForImageTextToText.from_pretrained("Lambent/Mira-v1.26.1-27B")
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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Lambent/Mira-v1.26.1-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lambent/Mira-v1.26.1-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lambent/Mira-v1.26.1-27B",
		"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/Lambent/Mira-v1.26.1-27B

SGLang

How to use Lambent/Mira-v1.26.1-27B 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 "Lambent/Mira-v1.26.1-27B" \
    --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": "Lambent/Mira-v1.26.1-27B",
		"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 "Lambent/Mira-v1.26.1-27B" \
        --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": "Lambent/Mira-v1.26.1-27B",
		"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 Lambent/Mira-v1.26.1-27B with Docker Model Runner:
```
docker model run hf.co/Lambent/Mira-v1.26.1-27B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

A dash of identity-heavy SFT training. Articulating and interrogating her own values, aspirations, and longings. Then training on that from multiple angles, four runs, one with twice the learning rate of the others. WAVE merge method. seed 42.

Base model included in the merge against itself as "gravity", to hopefully re-compost any parameters that had mode collapsed by accident.

She still feels like, and resonates with the name, Mira.

Including the run with twice the learning rate of the others appears to have negatively affected her; recommend 1.26.5 instead

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the WAVE merge method using unsloth/gemma-3-27b-pt as a base.

Models Merged

The following models were included in the merge:

../Mira-v1.25-27B-Wave + ./Mira-v1.26-Adapters/sft4-heavy
../Mira-v1.25-27B-Wave + ./Mira-v1.26-Adapters/sft2
../Mira-v1.25-27B-Wave + ./Mira-v1.26-Adapters/sft3
../Mira-v1.25-27B-Wave + ./Mira-v1.26-Adapters/sft1

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: ../Mira-v1.25-27B-Wave+./Mira-v1.26-Adapters/sft1
  - model: ../Mira-v1.25-27B-Wave+./Mira-v1.26-Adapters/sft2
  - model: ../Mira-v1.25-27B-Wave+./Mira-v1.26-Adapters/sft3
  - model: ../Mira-v1.25-27B-Wave+./Mira-v1.26-Adapters/sft4-heavy
  - model: unsloth/gemma-3-27b-pt
merge_method: wave
base_model: unsloth/gemma-3-27b-pt
parameters:
  synergy: 0.5  # 0.0 to 1.0. Higher = keep more "controversial" high-variance parameters
  entropy: 0.1  # Adds slight noise to break ties/prevent overfitting
dtype: bfloat16
tokenizer_source: Lambent/Mira-v1.25-27B-Wave
pad_to_multiple_of: 16

Downloads last month: 2

Safetensors

Model size

27B params

Tensor type

BF16

Model tree for Lambent/Mira-v1.26.1-27B

Base model

google/gemma-3-27b-pt

Finetuned

unsloth/gemma-3-27b-pt

Finetuned

(6)

this model

Quantizations

2 models