Instructions to use rizla/rizla-69 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rizla/rizla-69 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rizla/rizla-69")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("rizla/rizla-69") model = AutoModelForCausalLM.from_pretrained("rizla/rizla-69") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use rizla/rizla-69 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rizla/rizla-69" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rizla/rizla-69", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/rizla/rizla-69
- SGLang
How to use rizla/rizla-69 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 "rizla/rizla-69" \ --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": "rizla/rizla-69", "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 "rizla/rizla-69" \ --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": "rizla/rizla-69", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use rizla/rizla-69 with Docker Model Runner:
docker model run hf.co/rizla/rizla-69
Rizla-69
This is a crop of momo-qwen-72B
This repository contains a state-of-the-art machine learning model that promises to bring big changes to the field. The model is trained on [describe the dataset or type of data here].
License
This project is licensed under the terms of the Apache 2.0 license.
Model Architecture
The model uses [describe the model architecture here, e.g., a transformer-based architecture with a specific type of attention mechanism].
Training
The model was trained on [describe the hardware used, e.g., an NVIDIA Tesla P100 GPU] using [mention the optimization algorithm, learning rate, batch size, number of epochs, etc.].
Results
Our model achieved [mention the results here, e.g., an accuracy of 95% on the test set].
Usage
To use the model in your project, follow these steps:
- Install the Hugging Face Transformers library:
pip install transformers
- Downloads last month
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docker model run hf.co/rizla/rizla-69