Instructions to use DevHunterAI/RubiNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DevHunterAI/RubiNet with Transformers:

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

pipe = pipeline("text-generation", model="DevHunterAI/RubiNet")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("DevHunterAI/RubiNet", dtype="auto")

PEFT
How to use DevHunterAI/RubiNet with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DevHunterAI/RubiNet with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DevHunterAI/RubiNet"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DevHunterAI/RubiNet",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DevHunterAI/RubiNet

SGLang

How to use DevHunterAI/RubiNet 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 "DevHunterAI/RubiNet" \
    --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": "DevHunterAI/RubiNet",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "DevHunterAI/RubiNet" \
        --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": "DevHunterAI/RubiNet",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DevHunterAI/RubiNet with Docker Model Runner:
```
docker model run hf.co/DevHunterAI/RubiNet
```

DevHunterAI commited on Mar 28

Commit

9f0b563

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1 Parent(s): df1b672

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@@ -103,6 +103,8 @@ This repository is intended to host the RubiNet adapter release and related refe
 - `tokenizer_config.json`
 - `ministral_3b_hmc_chat.py`
 - `ministral_3b_hmc_server.py`
 - benchmark result JSON files
 This repository does **not** bundle the original base model weights. You need access to the base model `mistralai/Ministral-3-3B-Base-2512` in order to load this adapter.
@@ -132,6 +134,12 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 ## Chat Example
 ![RubiNet HMC architecture](./RubiNetHMC.png)
 RubiNet HMC architecture overview used in the local serving stack.

 - `tokenizer_config.json`
 - `ministral_3b_hmc_chat.py`
 - `ministral_3b_hmc_server.py`
+- `local.png`
+- `RubiNetHMC.png`
 - benchmark result JSON files
 This repository does **not** bundle the original base model weights. You need access to the base model `mistralai/Ministral-3-3B-Base-2512` in order to load this adapter.
 ## Chat Example
+![RubiNet local chat example](./local.png)
+Example local RubiNet chat interface screenshot.
+## Architecture Overview
 ![RubiNet HMC architecture](./RubiNetHMC.png)
 RubiNet HMC architecture overview used in the local serving stack.