Instructions to use ilsp/base_slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ilsp/base_slerp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ilsp/base_slerp")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("ilsp/base_slerp") model = AutoModelForMultimodalLM.from_pretrained("ilsp/base_slerp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ilsp/base_slerp with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ilsp/base_slerp" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ilsp/base_slerp", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ilsp/base_slerp
- SGLang
How to use ilsp/base_slerp 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 "ilsp/base_slerp" \ --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": "ilsp/base_slerp", "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 "ilsp/base_slerp" \ --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": "ilsp/base_slerp", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ilsp/base_slerp with Docker Model Runner:
docker model run hf.co/ilsp/base_slerp
cpt3_decay_strong__base_slerp_075
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the SLERP merge method.
Models Merged
The following models were included in the merge:
- /opt/dlami/nvme/models/merged/cpt3_decay_strong
- mistralai/Ministral-3-14B-Base-2512
Configuration
The following YAML configuration was used to produce this model:
name: cpt3_decay_strong__base_slerp_075
merge_method: slerp
base_model: mistralai/Ministral-3-14B-Base-2512
models:
- model: /opt/dlami/nvme/models/merged/cpt3_decay_strong
parameters:
t: 0.75
dtype: bfloat16
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Base model
mistralai/Ministral-3-14B-Base-2512