Instructions to use Statuo/LemonWizardv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Statuo/LemonWizardv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Statuo/LemonWizardv3")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Statuo/LemonWizardv3") model = AutoModelForCausalLM.from_pretrained("Statuo/LemonWizardv3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Statuo/LemonWizardv3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Statuo/LemonWizardv3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Statuo/LemonWizardv3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Statuo/LemonWizardv3
- SGLang
How to use Statuo/LemonWizardv3 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 "Statuo/LemonWizardv3" \ --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": "Statuo/LemonWizardv3", "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 "Statuo/LemonWizardv3" \ --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": "Statuo/LemonWizardv3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Statuo/LemonWizardv3 with Docker Model Runner:
docker model run hf.co/Statuo/LemonWizardv3
| base_model: | |
| - KatyTheCutie/LemonadeRP-4.5.3 | |
| - Replete-AI/WizardLM-2-7b | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| license: cc-by-nc-4.0 | |
|  | |
| # Intent | |
| The intent was to combine the excellent LemonadeRP-4.5.3 with WizardLM-2 in order to produce more effective uncensored content. While WizardLM-2 wouldn't balk at uncensored content, it would still falter in actually producing it whereas LemonadeRP didn't have this issue. The results are pretty good imo. There's a problem that if your response length is too long it will start to speak for the user but those usually disappear on swipes. | |
| I had originally not intended to release this model and instead keep it private. It's my first foray into doing merges at all and I didn't want to release a subpar model. However, after encouragement I've decided to unprivate it. Hope you all get some enjoyment out of it. | |
| # Prompt - Alpaca | |
| Using the Alpaca prompt seems to get good results. | |
| # Context Size - 8192 | |
| Haven't tested beyond this. Usual rule of thumb is that once you get up to 12k your responses tend to become less coherent and 16k is where things just devolve completely. | |
| # merge | |
| This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). | |
| ## Merge Details | |
| ### Merge Method | |
| This model was merged using the SLERP merge method. | |
| ### Models Merged | |
| The following models were included in the merge: | |
| * [KatyTheCutie/LemonadeRP-4.5.3](https://huggingface.co/KatyTheCutie/LemonadeRP-4.5.3) | |
| * [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b) | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| models: | |
| - model: Replete-AI/WizardLM-2-7b | |
| - model: KatyTheCutie/LemonadeRP-4.5.3 | |
| merge_method: slerp | |
| base_model: KatyTheCutie/LemonadeRP-4.5.3 | |
| dtype: bfloat16 | |
| parameters: | |
| t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers | |
| ``` |