Instructions to use lemon07r/RiverCub-Gemma-3-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lemon07r/RiverCub-Gemma-3-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="lemon07r/RiverCub-Gemma-3-27B")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("lemon07r/RiverCub-Gemma-3-27B")
model = AutoModelForMultimodalLM.from_pretrained("lemon07r/RiverCub-Gemma-3-27B", 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 lemon07r/RiverCub-Gemma-3-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lemon07r/RiverCub-Gemma-3-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lemon07r/RiverCub-Gemma-3-27B",
		"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/lemon07r/RiverCub-Gemma-3-27B

SGLang

How to use lemon07r/RiverCub-Gemma-3-27B 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 "lemon07r/RiverCub-Gemma-3-27B" \
    --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": "lemon07r/RiverCub-Gemma-3-27B",
		"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 "lemon07r/RiverCub-Gemma-3-27B" \
        --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": "lemon07r/RiverCub-Gemma-3-27B",
		"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 lemon07r/RiverCub-Gemma-3-27B with Docker Model Runner:
```
docker model run hf.co/lemon07r/RiverCub-Gemma-3-27B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

RiverCub-Gemma-3-27B

A slerp merge of what I believe to be the two best gemma 3 27b models after extensive testing of many. Unfortunately most finetunes of this model seem to make it come out worse than google's official instruct trained model, hence why I am using it in this slerp merge to keep some of it's magic. It really is quite good. Big tiger gemma v3 was surprisingly pretty good too, and seemed much less lobotomized compared to a lot of the other models I tested.

Quants

GGUFs

iMatrix

https://huggingface.co/mradermacher/RiverCub-Gemma-3-27B-i1-GGUF - Recommended

Static

Special Thanks

Big thanks to everyone over at the KoboldAI discord. The members there have helped me a ton with various things over the long while I've been there, even letting me borrow GPU hours on runpod for some testing at some point. ɛmpti gets today's special thanks in particular for helping me figure out how to get rid of the extra head that was carried over from drummer's model.. which seems to have been caused by an issue with axolotl.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

modules:
  text_decoder:
    slices:
      - sources:
          - model: unsloth/gemma-3-27b-it
            layer_range: [0, 62]
          - model: TheDrummer/Big-Tiger-Gemma-27B-v3
            layer_range: [0, 62]
  vision_tower:
    models:
      - model: unsloth/gemma-3-27b-it
  multi_modal_projector:
    models:
      - model: unsloth/gemma-3-27b-it
merge_method: slerp
base_model: unsloth/gemma-3-27b-it
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

Downloads last month: 27

Safetensors

Model size

27B params

Tensor type

BF16

Model tree for lemon07r/RiverCub-Gemma-3-27B

TheDrummer/Big-Tiger-Gemma-27B-v3

unsloth/gemma-3-27b-it

Merge model

this model

Finetunes

1 model

Quantizations

4 models