Instructions to use NewstaR/7B-Orfini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NewstaR/7B-Orfini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NewstaR/7B-Orfini")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NewstaR/7B-Orfini") model = AutoModelForCausalLM.from_pretrained("NewstaR/7B-Orfini") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use NewstaR/7B-Orfini with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NewstaR/7B-Orfini" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NewstaR/7B-Orfini", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/NewstaR/7B-Orfini
- SGLang
How to use NewstaR/7B-Orfini 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 "NewstaR/7B-Orfini" \ --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": "NewstaR/7B-Orfini", "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 "NewstaR/7B-Orfini" \ --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": "NewstaR/7B-Orfini", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use NewstaR/7B-Orfini with Docker Model Runner:
docker model run hf.co/NewstaR/7B-Orfini
| license: mit | |
| datasets: | |
| - Open-Orca/OpenOrca | |
| - conceptofmind/cot_submix_original | |
| - conceptofmind/t0_submix_original | |
| - conceptofmind/niv2_submix_original | |
| - conceptofmind/flan2021_submix_original | |
| - ehartford/dolphin | |
| language: | |
| - en | |
| tags: | |
| - merge | |
| - slerp | |
| inference: false | |
| metrics: | |
| - accuracy | |
| - bleu | |
| <h1 style="text-align: center">Orfini</h1> | |
| <h2 style="text-align: center">An experimental model</h2> | |
| <hr> | |
| ## Model Details | |
| Orfini is an experimental merged model created from the following three foundation models: | |
| - stabilityai/StableBeluga-7B | |
| - pankajmathur/orca_mini_v3_7b | |
| - AIDC-ai-business/Marcoroni-7B | |
| Orfini was created by merging the weights and architectures of these three models using a custom merging technique. No further fine-tuning was performed after the merge. | |
| Once the model obtains it's evaluation scores, then we'll know if it works or not. | |
| ## Intended Use | |
| As an experimental model, Orfini is intended for testing and research purposes only. It should not be used for production systems or to generate content for public use. | |
| ## Training Data | |
| Orfini inherits training data from its three foundation models: | |
| - StableBeluga-7B: COT, Niv2, t0, & FLAN2021 | |
| - dolphin-llama2-7b: Dolphin | |
| - Marcoroni-7B: OpenOrca | |
| ## Limitations | |
| As an untested merged model, Orfini has unknown capabilities and limitations. Potential issues include: | |
| - Instability due to merged architectures | |
| - Compounded bias and issues from all three foundation models | |
| - Decreased performance on some tasks compared to the foundation models | |
| Extensive testing is required to characterize Orfini's capabilities and limitations. | |
| ## Ethical Considerations | |
| - Orfini may exhibit harmful biases inherited from its training data | |
| - Output may be unreliable or manipulated due to instability | |
| - Experimental nature increases potential for misuse | |
| Use this model ethically and do not deploy it for sensitive applications. | |
| ## Contact Information | |
| Please report issues or concerns with this model to the creator for further investigation. |