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") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
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README.md
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tags:
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- mergekit
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- merge
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---
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# merge
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This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
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dtype: bfloat16
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parameters:
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t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers
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```
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tags:
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- mergekit
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- merge
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license: cc-by-nc-4.0
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---
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# Intent
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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.
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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.
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# Prompt - Alpaca
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Using the Alpaca prompt seems to get good results.
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# Context Size - 8192
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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.
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# merge
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This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
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dtype: bfloat16
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parameters:
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t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers
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```
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