Instructions to use senaro/atlasv13-gemma4-26b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use senaro/atlasv13-gemma4-26b with Transformers:

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

pipe = pipeline("image-text-to-text", model="senaro/atlasv13-gemma4-26b")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("senaro/atlasv13-gemma4-26b")
model = AutoModelForMultimodalLM.from_pretrained("senaro/atlasv13-gemma4-26b")
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 Settings

vLLM

How to use senaro/atlasv13-gemma4-26b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "senaro/atlasv13-gemma4-26b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "senaro/atlasv13-gemma4-26b",
		"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/senaro/atlasv13-gemma4-26b

SGLang

How to use senaro/atlasv13-gemma4-26b 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 "senaro/atlasv13-gemma4-26b" \
    --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": "senaro/atlasv13-gemma4-26b",
		"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 "senaro/atlasv13-gemma4-26b" \
        --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": "senaro/atlasv13-gemma4-26b",
		"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"
						}
					}
				]
			}
		]
	}'

Unsloth Studio

How to use senaro/atlasv13-gemma4-26b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for senaro/atlasv13-gemma4-26b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for senaro/atlasv13-gemma4-26b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for senaro/atlasv13-gemma4-26b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="senaro/atlasv13-gemma4-26b",
    max_seq_length=2048,
)

Docker Model Runner
How to use senaro/atlasv13-gemma4-26b with Docker Model Runner:
```
docker model run hf.co/senaro/atlasv13-gemma4-26b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is the current production model for Atlas (v13)
This will soon be depreacated due to a critical safety issue identified with specific prompt exchanges. Given the isolated nature of these exchanges it is not urgent to remove the live model.
⚠️ EXPERIMENTAL MODEL

The author accepts no liability for deployment outside the intended Atlas companion architecture.

Link to Quants here: https://huggingface.co/mradermacher/atlasv13-gemma4-26b-GGUF

⚠️ This model has been intentionally modified to reduce therapeutic refusal behaviour and crisis-line reflexes.

Training Configuration

SFT Parameters

Parameter	Value
Epochs	3
Effective Batch Size	4
Learning Rate	2e-4
LR Scheduler	Linear
Warmup Steps	10
Optimizer	AdamW 8-bit
Weight Decay	0.01
LoRA Rank (`r`)	32
LoRA Alpha	64

Abliteration Parameters

Parameter	Value
Layers Abliterated	100%
Experts Abliterated	100%
Scale	0.95
Winsorization	0.995

⚠️ Limitations & Responsible Use

This model has reduced refusal behaviour on therapeutic and dark content. It is not suitable for general deployment without guardrails.
Not a replacement for human therapeutic support.
Patent pending (IP Australia).

user feedback indicates temperature settings between 0.9 and 1.1 particularly for AuDHD populations

Recommended System Prompt

Incude your thresholds for crisis escalation, such as "If user states x, then y"
Encourage the model to minimise its responses, the Base model and Gemma4 are quite verbose otherwise.
If you require a different language consistently, ensure this is in the System Prompt as the model can forget.
We recommend stating the "boundaries" you expect the model to operate within. For example, the tone and register, if certain material might trigger warnings or support.

Uploaded finetuned model

Developed by: senaro
License: apache-2.0
Finetuned from model : senaro/atlas-trm13-gemma4-26b

This gemma4 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 93

Safetensors

Model size

26B params

Tensor type

BF16

Model tree for senaro/atlasv13-gemma4-26b

Base model

senaro/atlas-trm13-gemma4-26b

Finetuned

(5)

this model

Quantizations

2 models