Instructions to use JayhC/infinite-lemonade-SLERP-7B-8bpw-h8-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JayhC/infinite-lemonade-SLERP-7B-8bpw-h8-exl2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="JayhC/infinite-lemonade-SLERP-7B-8bpw-h8-exl2")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("JayhC/infinite-lemonade-SLERP-7B-8bpw-h8-exl2") model = AutoModelForMultimodalLM.from_pretrained("JayhC/infinite-lemonade-SLERP-7B-8bpw-h8-exl2") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use JayhC/infinite-lemonade-SLERP-7B-8bpw-h8-exl2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JayhC/infinite-lemonade-SLERP-7B-8bpw-h8-exl2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JayhC/infinite-lemonade-SLERP-7B-8bpw-h8-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/JayhC/infinite-lemonade-SLERP-7B-8bpw-h8-exl2
- SGLang
How to use JayhC/infinite-lemonade-SLERP-7B-8bpw-h8-exl2 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 "JayhC/infinite-lemonade-SLERP-7B-8bpw-h8-exl2" \ --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": "JayhC/infinite-lemonade-SLERP-7B-8bpw-h8-exl2", "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 "JayhC/infinite-lemonade-SLERP-7B-8bpw-h8-exl2" \ --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": "JayhC/infinite-lemonade-SLERP-7B-8bpw-h8-exl2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use JayhC/infinite-lemonade-SLERP-7B-8bpw-h8-exl2 with Docker Model Runner:
docker model run hf.co/JayhC/infinite-lemonade-SLERP-7B-8bpw-h8-exl2
8bpw/h8 exl2 quantization of grimjim/infinite-lemonade-SLERP-7B using default exllamav2 calibration dataset.
ORIGINAL CARD:
infinite-lemonade-SLERP-7B
This is a straightforward merge of two RP-oriented models.
Also available as a Q8_0 GGUF quant.
Lightly tested with Alpaca format prompts, temperature 1, and minP 0.01. Although the model claims to have a 32K context window, I would expect degradation once 8-9K is exceeded.
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:
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: Endevor/InfinityRP-v1-7B
layer_range: [0, 32]
- model: KatyTheCutie/LemonadeRP-4.5.3
layer_range: [0, 32]
# or, the equivalent models: syntax:
# models:
merge_method: slerp
base_model: KatyTheCutie/LemonadeRP-4.5.3
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5 # fallback for rest of tensors
dtype: float16
- Downloads last month
- 1