Instructions to use Aryanne/WestSenzu-Swap-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aryanne/WestSenzu-Swap-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Aryanne/WestSenzu-Swap-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Aryanne/WestSenzu-Swap-7B") model = AutoModelForCausalLM.from_pretrained("Aryanne/WestSenzu-Swap-7B") - llama-cpp-python
How to use Aryanne/WestSenzu-Swap-7B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Aryanne/WestSenzu-Swap-7B", filename="f16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Aryanne/WestSenzu-Swap-7B with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Aryanne/WestSenzu-Swap-7B:F16 # Run inference directly in the terminal: llama-cli -hf Aryanne/WestSenzu-Swap-7B:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Aryanne/WestSenzu-Swap-7B:F16 # Run inference directly in the terminal: llama-cli -hf Aryanne/WestSenzu-Swap-7B:F16
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Aryanne/WestSenzu-Swap-7B:F16 # Run inference directly in the terminal: ./llama-cli -hf Aryanne/WestSenzu-Swap-7B:F16
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Aryanne/WestSenzu-Swap-7B:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Aryanne/WestSenzu-Swap-7B:F16
Use Docker
docker model run hf.co/Aryanne/WestSenzu-Swap-7B:F16
- LM Studio
- Jan
- vLLM
How to use Aryanne/WestSenzu-Swap-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Aryanne/WestSenzu-Swap-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Aryanne/WestSenzu-Swap-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Aryanne/WestSenzu-Swap-7B:F16
- SGLang
How to use Aryanne/WestSenzu-Swap-7B 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 "Aryanne/WestSenzu-Swap-7B" \ --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": "Aryanne/WestSenzu-Swap-7B", "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 "Aryanne/WestSenzu-Swap-7B" \ --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": "Aryanne/WestSenzu-Swap-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use Aryanne/WestSenzu-Swap-7B with Ollama:
ollama run hf.co/Aryanne/WestSenzu-Swap-7B:F16
- Unsloth Studio new
How to use Aryanne/WestSenzu-Swap-7B with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Aryanne/WestSenzu-Swap-7B to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Aryanne/WestSenzu-Swap-7B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Aryanne/WestSenzu-Swap-7B to start chatting
- Docker Model Runner
How to use Aryanne/WestSenzu-Swap-7B with Docker Model Runner:
docker model run hf.co/Aryanne/WestSenzu-Swap-7B:F16
- Lemonade
How to use Aryanne/WestSenzu-Swap-7B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Aryanne/WestSenzu-Swap-7B:F16
Run and chat with the model
lemonade run user.WestSenzu-Swap-7B-F16
List all available models
lemonade list
It's experimental, but seems fine for me, I didn't run it deeply yet but should be good for Role-play 😈 considering the two merged models, feel free to leave a suggestion or feedback.
This is a merge of pre-trained language models created using mergekit(my experimental branch swapping here )
Merge Details
Merge Method
This model was merged using the task_swapping merge method using NeuralNovel/Senzu-7B-v0.1-DPO as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
merge_method: task_swapping
base_model: NeuralNovel/Senzu-7B-v0.1-DPO
models:
- model: senseable/WestLake-7B-v2
parameters:
weight: 0.75
diagonal_offset: 2 #it doesn't do anything when you use random_mask
random_mask: 0.3333
random_mask_seed: 98557
dtype: bfloat16
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 67.28 |
| AI2 Reasoning Challenge (25-Shot) | 68.34 |
| HellaSwag (10-Shot) | 85.70 |
| MMLU (5-Shot) | 64.14 |
| TruthfulQA (0-shot) | 50.43 |
| Winogrande (5-shot) | 82.48 |
| GSM8k (5-shot) | 52.62 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard68.340
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard85.700
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.140
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard50.430
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard82.480
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard52.620