Instructions to use OddTheGreat/Mars_27B_V.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OddTheGreat/Mars_27B_V.1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="OddTheGreat/Mars_27B_V.1")
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("OddTheGreat/Mars_27B_V.1")
model = AutoModelForMultimodalLM.from_pretrained("OddTheGreat/Mars_27B_V.1", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OddTheGreat/Mars_27B_V.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OddTheGreat/Mars_27B_V.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OddTheGreat/Mars_27B_V.1",
		"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/OddTheGreat/Mars_27B_V.1

SGLang

How to use OddTheGreat/Mars_27B_V.1 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 "OddTheGreat/Mars_27B_V.1" \
    --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": "OddTheGreat/Mars_27B_V.1",
		"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 "OddTheGreat/Mars_27B_V.1" \
        --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": "OddTheGreat/Mars_27B_V.1",
		"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 OddTheGreat/Mars_27B_V.1 with Docker Model Runner:
```
docker model run hf.co/OddTheGreat/Mars_27B_V.1
```

OddTheGreat commited on Nov 11, 2025

Commit

8de306b

verified ·

1 Parent(s): 2d962c8

Update README.md

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README.md +31 -26

README.md CHANGED Viewed

@@ -1,26 +1,31 @@
----
-base_model:
-- TheDrummer/Big-Tiger-Gemma-27B-v3
-- Tesslate/Synthia-S1-27b
-- summykai/gemma3-27b-abliterated-dpo
-library_name: transformers
-tags:
-- mergekit
-- merge
----
-# Planetoid_27B_V.3
-This is a merge of pre-trained Gemma3 language models.
-Goal of this merge was to create high quality roleplay model for complex cards and scenarios.
-Result i can call good. Model is smart, smarter than 24b mistral. Writing style is pleastant to read, instructions followed.
-Gemma context attention is flawless, every small detail is remembered.
-Cliches are present, though it different from Mistral's.
-I had only problem with this model - it's too memory hungry, even on Q4_K_S and 8k context only half og layers fills my 16gb.
-Tested on T0.8, XTC off.

+---
+base_model:
+- TheDrummer/Big-Tiger-Gemma-27B-v3
+- Tesslate/Synthia-S1-27b
+- summykai/gemma3-27b-abliterated-dpo
+library_name: transformers
+tags:
+- mergekit
+- merge
+- creative
+- roleplay
+language:
+- en
+- ru
+---
+# Mars_27B_V.1
+This is a merge of pre-trained Gemma3 language models.
+Goal of this merge was to create high quality roleplay model for complex cards and scenarios. ERP too.
+The result i can call good. Model is smart, smarter than 24b mistral. Writing style is pleastant to read, instructions followed.
+Gemma context attention is flawless, every small detail is remembered.
+Cliches are present, though they are different from Mistral's.
+Ru is supported, good enough for roleplay, good enough as assistant.
+I had only problem with this model - it's too memory hungry, even on Q4_K_S and 8k context only half of layers fits it my 16gb vram.
+Tested on T0.8, XTC off.