Instructions to use TheBigBlender/EstopianMaid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheBigBlender/EstopianMaid with Transformers:

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

pipe = pipeline("text-generation", model="TheBigBlender/EstopianMaid")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TheBigBlender/EstopianMaid")
model = AutoModelForCausalLM.from_pretrained("TheBigBlender/EstopianMaid")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TheBigBlender/EstopianMaid with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheBigBlender/EstopianMaid"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TheBigBlender/EstopianMaid",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TheBigBlender/EstopianMaid

SGLang

How to use TheBigBlender/EstopianMaid 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 "TheBigBlender/EstopianMaid" \
    --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": "TheBigBlender/EstopianMaid",
		"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 "TheBigBlender/EstopianMaid" \
        --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": "TheBigBlender/EstopianMaid",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TheBigBlender/EstopianMaid with Docker Model Runner:
```
docker model run hf.co/TheBigBlender/EstopianMaid
```

EstopianMaid

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

Merge Details

Merge Method

This model was merged using the task arithmetic merge method using TheBloke/Llama-2-13B-fp16 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: TheBloke/Llama-2-13B-fp16
dtype: float16
merge_method: task_arithmetic
slices:
- sources:
  - layer_range: [0, 40]
    model: TheBloke/Llama-2-13B-fp16
  - layer_range: [0, 40]
    model: BlueNipples/TimeCrystal-l2-13B
    parameters:
      weight: 0.75
  - layer_range: [0, 40]
    model: cgato/Thespis-13b-DPO-v0.7
    parameters:
      weight: 0.23
  - layer_range: [0, 40]
    model: KoboldAI/LLaMA2-13B-Estopia
    parameters:
      weight: 0.15
  - layer_range: [0, 40]
    model: NeverSleep/Noromaid-13B-0.4-DPO
    parameters:
      weight: 0.2
  - layer_range: [0, 40]
    model: Doctor-Shotgun/cat-v1.0-13b
    parameters:
      weight: 0.03