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

f2f8e83

verified ·

1 Parent(s): c3e1c66

Update README.md

Browse files

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README.md +3 -5

README.md CHANGED Viewed

@@ -14,9 +14,7 @@ tags:
 - reasoning
 - mathematical-reasoning
 datasets:
-- piqa
-- ai2_arc
-- gsm8k
 base_model:
 - mistralai/Ministral-3-3B-Base-2512
 library_name: transformers
@@ -57,7 +55,7 @@ model-index:
     metrics:
     - type: accuracy
       name: Accuracy
-      value: 24.0
 ---
 # RubiNet
@@ -157,4 +155,4 @@ RubiNet is a fine-tuned conversational adaptation derived from `mistralai/Minist
 ## Repository Notes
-If you publish this repository publicly, keep the model title as **RubiNet** and place extra technical details such as benchmark scores, language coverage, and architecture hints in the tags and description rather than in the title.

 - reasoning
 - mathematical-reasoning
 datasets:
+- HuggingFaceH4/ultrachat_200k
 base_model:
 - mistralai/Ministral-3-3B-Base-2512
 library_name: transformers
     metrics:
     - type: accuracy
       name: Accuracy
+      value: 24
 ---
 # RubiNet
 ## Repository Notes
+If you publish this repository publicly, keep the model title as **RubiNet** and place extra technical details such as benchmark scores, language coverage, and architecture hints in the tags and description rather than in the title.