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

Libraries

How to use Aryanne/Westest-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Aryanne/Westest-7B")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Aryanne/Westest-7B")
model = AutoModelForCausalLM.from_pretrained("Aryanne/Westest-7B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Aryanne/Westest-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/Westest-7B:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf Aryanne/Westest-7B:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Aryanne/Westest-7B:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf Aryanne/Westest-7B:Q3_K_M

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/Westest-7B:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf Aryanne/Westest-7B:Q3_K_M

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/Westest-7B:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Aryanne/Westest-7B:Q3_K_M

Use Docker

docker model run hf.co/Aryanne/Westest-7B:Q3_K_M

LM Studio
Jan

vLLM

How to use Aryanne/Westest-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Aryanne/Westest-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/Westest-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Aryanne/Westest-7B:Q3_K_M

SGLang

How to use Aryanne/Westest-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/Westest-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/Westest-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/Westest-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/Westest-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Aryanne/Westest-7B with Ollama:
```
ollama run hf.co/Aryanne/Westest-7B:Q3_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Aryanne/Westest-7B with Docker Model Runner:
```
docker model run hf.co/Aryanne/Westest-7B:Q3_K_M
```

Lemonade

How to use Aryanne/Westest-7B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Aryanne/Westest-7B:Q3_K_M

Run and chat with the model

lemonade run user.Westest-7B-Q3_K_M

List all available models

lemonade list

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

merged

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the task_anysize merge method using senseable/WestLake-7B-v2 as a base.

Models Merged

The following models were included in the merge:

chargoddard/piano-medley-7b

Configuration

The following YAML configuration was used to produce this model:

base_model:
  model:
    path: senseable/WestLake-7B-v2
dtype: bfloat16
merge_method: task_anysize
slices:
- sources:
  - layer_range: [0, 32]
    model:
      model:
        path: chargoddard/piano-medley-7b
    parameters:
      weight: 0.55
  - layer_range: [0, 32]
    model:
      model:
        path: senseable/WestLake-7B-v2

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	74.03
AI2 Reasoning Challenge (25-Shot)	72.18
HellaSwag (10-Shot)	88.52
MMLU (5-Shot)	64.43
TruthfulQA (0-shot)	66.72
Winogrande (5-shot)	86.58
GSM8k (5-shot)	65.73

Downloads last month: 308

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for Aryanne/Westest-7B

chargoddard/piano-medley-7b

senseable/WestLake-7B-v2

Merge model

this model

Quantizations

2 models

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

72.180
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

88.520
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.430
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

66.720
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

86.580
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

65.730