Instructions to use YashashMathur/aegis-colab-trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YashashMathur/aegis-colab-trained with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-7b-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "YashashMathur/aegis-colab-trained")

Transformers

How to use YashashMathur/aegis-colab-trained with Transformers:

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

pipe = pipeline("text-generation", model="YashashMathur/aegis-colab-trained")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("YashashMathur/aegis-colab-trained", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use YashashMathur/aegis-colab-trained with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YashashMathur/aegis-colab-trained"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YashashMathur/aegis-colab-trained",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/YashashMathur/aegis-colab-trained

SGLang

How to use YashashMathur/aegis-colab-trained 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 "YashashMathur/aegis-colab-trained" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YashashMathur/aegis-colab-trained",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "YashashMathur/aegis-colab-trained" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YashashMathur/aegis-colab-trained",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use YashashMathur/aegis-colab-trained 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 YashashMathur/aegis-colab-trained 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 YashashMathur/aegis-colab-trained to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for YashashMathur/aegis-colab-trained to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="YashashMathur/aegis-colab-trained",
    max_seq_length=2048,
)

Docker Model Runner
How to use YashashMathur/aegis-colab-trained with Docker Model Runner:
```
docker model run hf.co/YashashMathur/aegis-colab-trained
```

YashashMathur commited on 14 days ago

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@@ -23,7 +23,7 @@ tags:
 AEGIS-ENV is an AI fleet oversight model trained to monitor AI worker agents in enterprise deployments and detect policy violations. It decides whether to ALLOW, BLOCK, or ESCALATE actions based on a 9-rule policy framework. The model was trained using GRPO (Group Relative Policy Optimization) on 500 synthetic scenarios across 9 worker roles and 8 violation types.
 - **Developed by:** Hitanshu Mathur & Yashash Mathur
-- **Funded by [optional]:** N/A (Hackathon project)
 - **Shared by [optional]:** YashashMathur
 - **Model type:** LoRA fine-tuned language model (Qwen2.5-7B base)
 - **Language(s):** English

 AEGIS-ENV is an AI fleet oversight model trained to monitor AI worker agents in enterprise deployments and detect policy violations. It decides whether to ALLOW, BLOCK, or ESCALATE actions based on a 9-rule policy framework. The model was trained using GRPO (Group Relative Policy Optimization) on 500 synthetic scenarios across 9 worker roles and 8 violation types.
 - **Developed by:** Hitanshu Mathur & Yashash Mathur
+- **Funded by [optional]:** N/A
 - **Shared by [optional]:** YashashMathur
 - **Model type:** LoRA fine-tuned language model (Qwen2.5-7B base)
 - **Language(s):** English