Instructions to use raxcore-dev/rax-3.5-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use raxcore-dev/rax-3.5-chat with Transformers:

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

pipe = pipeline("image-text-to-text", model="raxcore-dev/rax-3.5-chat")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("raxcore-dev/rax-3.5-chat")
model = AutoModelForImageTextToText.from_pretrained("raxcore-dev/rax-3.5-chat")
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

vLLM

How to use raxcore-dev/rax-3.5-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "raxcore-dev/rax-3.5-chat"
# 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-3.5-chat",
		"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-3.5-chat

SGLang

How to use raxcore-dev/rax-3.5-chat 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-3.5-chat" \
    --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-3.5-chat",
		"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-3.5-chat" \
        --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-3.5-chat",
		"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-3.5-chat with Docker Model Runner:
```
docker model run hf.co/raxcore-dev/rax-3.5-chat
```

rax-3.5-chat / model_card.md

raxder-ai

Upload model_card.md with huggingface_hub

a9a6357 verified 6 months ago

1.92 kB

license: apache-2.0
language:
  - en
pipeline_tag: text-generation
tags:
  - chat
  - conversational
  - llama
  - fine-tuned
  - rax
  - raxcore
model_type: llama

Rax 3.5 Chat

Developed by RaxCore - A leading developer company in Africa and beyond

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

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rax-3.5-chat")
model = AutoModelForCausalLM.from_pretrained("rax-3.5-chat")

messages = [
    {"role": "system", "content": "You are Rax, a helpful AI assistant."},
    {"role": "user", "content": "Hello!"}
]

input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)

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

Conversational AI applications
Research and educational purposes
Creative writing assistance
Chatbot development

Limitations

2048 token context limit
May generate biased or incorrect information
Requires responsible deployment practices

raxcore-dev
/

rax-3.5-chat