Instructions to use nintwentydo/Razorback-12B-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nintwentydo/Razorback-12B-v0.1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="nintwentydo/Razorback-12B-v0.1")
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("nintwentydo/Razorback-12B-v0.1")
model = AutoModelForMultimodalLM.from_pretrained("nintwentydo/Razorback-12B-v0.1", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nintwentydo/Razorback-12B-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nintwentydo/Razorback-12B-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nintwentydo/Razorback-12B-v0.1",
		"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/nintwentydo/Razorback-12B-v0.1

SGLang

How to use nintwentydo/Razorback-12B-v0.1 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 "nintwentydo/Razorback-12B-v0.1" \
    --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": "nintwentydo/Razorback-12B-v0.1",
		"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 "nintwentydo/Razorback-12B-v0.1" \
        --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": "nintwentydo/Razorback-12B-v0.1",
		"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 nintwentydo/Razorback-12B-v0.1 with Docker Model Runner:
```
docker model run hf.co/nintwentydo/Razorback-12B-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Razorback 12B v0.1

Update: Use v0.2 nintwentydo/Razorback-12B-v0.2

This is a first pass attempt to merge Mistral Nemo finetunes with Pixtral 12B. Has not been fully tested yet other than confirming it can understand vision input and output coherent text. May be unstable for all we know lol.

Pixtral 12B as base with TheDrummer's Rocinante and UnslopNemo finetunes merged in.

In The Expanse the Razorback is a ship fitted with engines way bigger than a ship of its size would normally have. Thought it was a fitting way to celebrate TheDrummer's models supercharging Pixtral. 😉

Credits

Mistral for mistralai/Pixtral-12B-2409
Unsloth for unsloth/Pixtral-12B-2409 transformers conversion
TheDrummer for TheDrummer/Rocinante-12B-v1.1
TheDrummer for TheDrummer/UnslopNemo-12B-v3

Downloads last month: 17

Safetensors

Model size

13B params

Tensor type

BF16

Model tree for nintwentydo/Razorback-12B-v0.1

TheDrummer/Rocinante-12B-v1.1

TheDrummer/UnslopNemo-12B-v3

mistralai/Pixtral-12B-2409

Merge model

this model

Quantizations

1 model