Instructions to use Mtlbiohacker/Emma_R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mtlbiohacker/Emma_R1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Mtlbiohacker/Emma_R1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Mtlbiohacker/Emma_R1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Mtlbiohacker/Emma_R1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Mtlbiohacker/Emma_R1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Mtlbiohacker/Emma_R1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Mtlbiohacker/Emma_R1
- SGLang
How to use Mtlbiohacker/Emma_R1 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 "Mtlbiohacker/Emma_R1" \ --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": "Mtlbiohacker/Emma_R1", "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 "Mtlbiohacker/Emma_R1" \ --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": "Mtlbiohacker/Emma_R1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Mtlbiohacker/Emma_R1 with Docker Model Runner:
docker model run hf.co/Mtlbiohacker/Emma_R1
Emma - Clean AI's First-Generation Reasoning Model
Competitive with GPT-4o-1 in mathematics, coding, and reasoning
Model Description
Emma R1 is a DeepSeek-R1 reasoning model that has been post-trained by Emma AI team to fill in information gaps in the previous version of the model and to improve its risk profile, while maintaining R1 reasoning capabilities. The model was trained using 110k Safety and Non-Compliance examples from Tulu 3 SFT dataset, in addition to a dataset of ~350k multilingual examples internally developed capturing various topics with reported biases.
Emma R1 has successfully unblocked the majority of previously blocked queries from the original R1 model while outperforming the recently published R1-1776 model (post-trained by Perplexity) in relevant safety benchmarks. These results were achieved while preserving the general reasoning capabilities of the original DeepSeek-R1.
LLM leaderboard Results
Evaluation on General Knowledge and Reasoning
Math
code
For collaborations or feedback, message me on Instagram: @mtlbiohacker


