Instructions to use Karko/Proctora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Karko/Proctora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Karko/Proctora")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Karko/Proctora") model = AutoModelForCausalLM.from_pretrained("Karko/Proctora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Karko/Proctora with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Karko/Proctora" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Karko/Proctora", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Karko/Proctora
- SGLang
How to use Karko/Proctora 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 "Karko/Proctora" \ --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": "Karko/Proctora", "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 "Karko/Proctora" \ --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": "Karko/Proctora", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Karko/Proctora with Docker Model Runner:
docker model run hf.co/Karko/Proctora
Commit History
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Adding Evaluation Results
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Verified signatureProctora is a MoE model made of - OpenPipe/mistral-ft-optimized-1227 as a base model - SanjiWatsuki/Kunoichi-7B as a first expert dedicated to RP tasks. - samir-fama/SamirGPT-v1 as a second expert for factual answers. I do not have yet any metrics for this model but subjective ones. It was made at first out of curiosity and experimentation. My goal is to produce a model excellent at being a game master for RPG sessions. However being dissatisfied with the existing evaluation tool-sets, I decided to create my own (still a WIP on 01/16/24). And among my collection of models of small/medium models Proctora gave me the best results to evaluate the answers produced by other LLMs. Therefore, I surprisingly settled with it and gave it an appropriate name according to the task. TLDR: Proctora is a tool for a tool! I doubt this model will be useful for the community. I publish it for the sake of transparency in my creative process.596122d -
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