Instructions to use rizla/rizla-9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rizla/rizla-9 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rizla/rizla-9")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("rizla/rizla-9") model = AutoModelForCausalLM.from_pretrained("rizla/rizla-9") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use rizla/rizla-9 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rizla/rizla-9" # 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-9", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/rizla/rizla-9
- SGLang
How to use rizla/rizla-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 "rizla/rizla-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": "rizla/rizla-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 "rizla/rizla-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": "rizla/rizla-9", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use rizla/rizla-9 with Docker Model Runner:
docker model run hf.co/rizla/rizla-9
rizla9
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the passthrough merge method using /home/ubuntu/nvm/jas as a base.
Models Merged
The following models were included in the merge:
- /home/ubuntu/garten
Configuration
The following YAML configuration was used to produce this model:
base_model:
model:
path: /home/ubuntu/jas
dtype: float16
merge_method: passthrough
slices:
- sources:
- layer_range: [0, 29]
model:
model:
path: /home/ubuntu/garten
- sources:
- layer_range: [8, 32]
model:
model:
path: /home/ubuntu/jas
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