Instructions to use Aryanne/WestSenzu-Swap-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Aryanne/WestSenzu-Swap-7B with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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

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

Atomic Chat
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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:

senseable/WestLake-7B-v2

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