Instructions to use raxcore-dev/Rax-4.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use raxcore-dev/Rax-4.5 with Transformers:

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

pipe = pipeline("image-text-to-text", model="raxcore-dev/Rax-4.5")
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("raxcore-dev/Rax-4.5")
model = AutoModelForMultimodalLM.from_pretrained("raxcore-dev/Rax-4.5")
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 raxcore-dev/Rax-4.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "raxcore-dev/Rax-4.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "raxcore-dev/Rax-4.5",
		"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/raxcore-dev/Rax-4.5

SGLang

How to use raxcore-dev/Rax-4.5 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 "raxcore-dev/Rax-4.5" \
    --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": "raxcore-dev/Rax-4.5",
		"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 "raxcore-dev/Rax-4.5" \
        --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": "raxcore-dev/Rax-4.5",
		"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 raxcore-dev/Rax-4.5 with Docker Model Runner:
```
docker model run hf.co/raxcore-dev/Rax-4.5
```

raxder-ai commited on Nov 27, 2025

Commit

a9a6357

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@@ -19,7 +19,7 @@ model_type: llama
 ## Model Description
-Rax 3.5 Chat is a conversational AI model based on the Llama architecture, specifically optimized for chat interactions and dialogue generation. This model represents several days of careful development to enhance conversational capabilities.
 ## Quick Start
@@ -41,9 +41,10 @@ outputs = model.generate(**inputs, max_new_tokens=256)
 ## Model Details
-- **Architecture**: Llama (1.1B parameters)
 - **Context Length**: 2048 tokens
-- **Training**: Developed by RaxCore for conversational AI
 - **License**: Apache 2.0
 ## Intended Use

 ## Model Description
+Rax 3.5 Chat is an extensively enhanced conversational AI model featuring breakthrough improvements developed by RaxCore. Built upon the Llama architecture with TinyLlama as foundation, this model incorporates proprietary optimization techniques, advanced training methodologies, and cultural context awareness that significantly exceed baseline performance.
 ## Quick Start
 ## Model Details
+- **Architecture**: Enhanced Llama (1.1B parameters with RaxCore optimizations)
 - **Context Length**: 2048 tokens
+- **Development**: Extensively enhanced by RaxCore with proprietary improvements
+- **Base**: TinyLlama foundation with significant RaxCore upgrades
 - **License**: Apache 2.0
 ## Intended Use