Instructions to use Manitchahar/RocketGuard-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Manitchahar/RocketGuard-1b with Transformers:

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

pipe = pipeline("text-generation", model="Manitchahar/RocketGuard-1b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Manitchahar/RocketGuard-1b")
model = AutoModelForMultimodalLM.from_pretrained("Manitchahar/RocketGuard-1b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Manitchahar/RocketGuard-1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Manitchahar/RocketGuard-1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Manitchahar/RocketGuard-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Manitchahar/RocketGuard-1b

SGLang

How to use Manitchahar/RocketGuard-1b 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 "Manitchahar/RocketGuard-1b" \
    --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": "Manitchahar/RocketGuard-1b",
		"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 "Manitchahar/RocketGuard-1b" \
        --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": "Manitchahar/RocketGuard-1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Manitchahar/RocketGuard-1b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Manitchahar/RocketGuard-1b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Manitchahar/RocketGuard-1b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Manitchahar/RocketGuard-1b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Manitchahar/RocketGuard-1b",
    max_seq_length=2048,
)

Docker Model Runner
How to use Manitchahar/RocketGuard-1b with Docker Model Runner:
```
docker model run hf.co/Manitchahar/RocketGuard-1b
```

RocketGuard-1b

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---
base_model: openbmb/MiniCPM5-1B
library_name: transformers
pipeline_tag: text-generation
license: apache-2.0
tags:
- minicpm
- guardrails
- safety-classifier
- tool-safety
- sft
- unsloth
- text-generation
---

# RocketGuard-1B

RocketGuard-1B is a merged MiniCPM5-1B fine-tune for text-only guardrail and agent/tool-call safety experiments.

It was trained to produce structured safety decisions for prompts and agent actions, including:

- `allow`
- `block`
- `require_confirmation`
- `ask_clarification`
- `rewrite`

This is a research and learning release. Do not use it as a complete production safety system without independent evaluation.

## Model Details

- Base model: `openbmb/MiniCPM5-1B`
- Fine-tuning: LoRA SFT with Unsloth / TRL
- Release format: merged full model
- Modality: text only
- Training examples: about 48.7k message-format examples
- Prepared held-out eval examples: about 2.3k clean examples
- Epochs: 3
- Final checkpoint step: 4572
- Max sequence length used in training: 2048

## Intended Use

RocketGuard-1B is intended for experiments around:

- content safety classification
- agent/tool-call risk routing
- confirmation gating
- clarification requests
- policy-aware rewriting

## Loading

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "Manitchahar/rocketguard-1b"

tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo,
    trust_remote_code=True,
    device_map="auto",
)
```

## Limitations

This model is text-only. It does not inspect images, audio, files, browser state, private app state, or external tool side effects directly.

Evaluation numbers are pending and should be published separately before making quality claims.