Instructions to use digitous/Javalion-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use digitous/Javalion-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="digitous/Javalion-R")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("digitous/Javalion-R") model = AutoModelForCausalLM.from_pretrained("digitous/Javalion-R") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use digitous/Javalion-R with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "digitous/Javalion-R" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "digitous/Javalion-R", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/digitous/Javalion-R
- SGLang
How to use digitous/Javalion-R 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 "digitous/Javalion-R" \ --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": "digitous/Javalion-R", "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 "digitous/Javalion-R" \ --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": "digitous/Javalion-R", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use digitous/Javalion-R with Docker Model Runner:
docker model run hf.co/digitous/Javalion-R
| license: creativeml-openrail-m | |
| Javalion-R is a penta merge of KoboldAI's GPT-J classics + PygmalionAI's Pygmalion6b; | |
| ((Janeway + Shinen) + (Skein + Pygmalion)) + GPT-R. | |
| Janeway + Shinen is listed under JANIN-GPTJ. Skein + Pygmalion is listed under SKEGMA-GPTJ. | |
| GPT-R itself is a 60/40 merge of two instruct research models (see digitous/GPT-R for full credits). | |
| This 5x+ merge is not intended for minors, as it can produce NC-17+ content. | |
| This model differs from Javelin-R by substituting the Adventure model with Pygmalion, as Adventure is rendered redundant in training data by Skein. | |
| Javalion-R is a research artefact with dual purpose for entertainment as well as an intended example of potential value instruct can bring when combined with models of a different purpose through the use of weight sum merge technology. | |
| Mileage mat vary. No refunds best wishes. Mainly intended to be utilized with Open Source KoboldAI software. Optimal sampler and settings not determined. Feedback Welcome! | |
| https://github.com/KoboldAI/KoboldAI-Client |