Text Generation
Transformers
Safetensors
English
mistral
4-bit precision
AWQ
roleplay
text-generation-inference
awq
Instructions to use solidrust/LemonadeRP-4.5.3-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use solidrust/LemonadeRP-4.5.3-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="solidrust/LemonadeRP-4.5.3-AWQ")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("solidrust/LemonadeRP-4.5.3-AWQ") model = AutoModelForCausalLM.from_pretrained("solidrust/LemonadeRP-4.5.3-AWQ") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use solidrust/LemonadeRP-4.5.3-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "solidrust/LemonadeRP-4.5.3-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "solidrust/LemonadeRP-4.5.3-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/solidrust/LemonadeRP-4.5.3-AWQ
- SGLang
How to use solidrust/LemonadeRP-4.5.3-AWQ 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 "solidrust/LemonadeRP-4.5.3-AWQ" \ --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": "solidrust/LemonadeRP-4.5.3-AWQ", "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 "solidrust/LemonadeRP-4.5.3-AWQ" \ --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": "solidrust/LemonadeRP-4.5.3-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use solidrust/LemonadeRP-4.5.3-AWQ with Docker Model Runner:
docker model run hf.co/solidrust/LemonadeRP-4.5.3-AWQ
KatyTheCutie/LemonadeRP-4.5.3 AWQ
- Model creator: KatyTheCutie
- Original model: LemonadeRP-4.5.3
Model Summary
8192 context length. - Reports of context up-to 32K working!
7B roleplay focused model, creativity and less cliché is the focus of this merge.
SillyTavern settings:

- NeverSleep/Noromaid-7B-0.4-DPO
- cgato/Thespis-7b-v0.5-SFTTest-2Epoch
- NurtureAI/neural-chat-7b-v3-1-16k
- cgato/Thespis-CurtainCall-7b-v0.2.2
- tavtav/eros-7b-test
- Downloads last month
- 2
Model tree for solidrust/LemonadeRP-4.5.3-AWQ
Base model
NeverSleep/Noromaid-7B-0.4-DPO Finetuned
KatyTheCutie/LemonadeRP-4.5.3
