Instructions to use James7765/WilR_9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use James7765/WilR_9 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="James7765/WilR_9")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("James7765/WilR_9") model = AutoModelForCausalLM.from_pretrained("James7765/WilR_9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use James7765/WilR_9 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "James7765/WilR_9" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "James7765/WilR_9", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/James7765/WilR_9
- SGLang
How to use James7765/WilR_9 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 "James7765/WilR_9" \ --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": "James7765/WilR_9", "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 "James7765/WilR_9" \ --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": "James7765/WilR_9", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use James7765/WilR_9 with Docker Model Runner:
docker model run hf.co/James7765/WilR_9
| tags: | |
| - text-generation-inference | |
| - transformers | |
| license: apache-2.0 | |
| language: | |
| - en | |
| Meet our powerful AI model! π With its advanced capabilities, it can understand and generate human-like text, making it an ideal tool for various applications. π» | |
| *Key Features:* | |
| - *π‘ Advanced Language Understanding*: Our AI model is trained on a vast amount of text data, enabling it to comprehend complex queries and provide relevant responses. π | |
| - *π High Accuracy*: With its advanced algorithms and training data, our AI model delivers high accuracy and reliability in its responses. β | |
| - *π Flexibility*: Our model can be used for a wide range of applications, from answering questions to generating text and more. π€ | |
| - *π Knowledge Base*: Our AI model has been trained on a large dataset, providing a broad knowledge base to draw from. π | |
| # new ai Model | |
| - **Developed by:** Nathan | |
| - **License:** apache-2.0 | |
| - **by Spectra.inc LTD jamaican ai company** | |
| [<img src="https://raw.githubusercontent.com/Wisk-inc/wisk.githujsjs/refs/heads/main/sparkles.png" width="200"/>](https://github.com/unslothai/unsloth) |