Instructions to use ddh0/EstopianOrcaMaid-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ddh0/EstopianOrcaMaid-13b with Transformers:

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

pipe = pipeline("text-generation", model="ddh0/EstopianOrcaMaid-13b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ddh0/EstopianOrcaMaid-13b")
model = AutoModelForCausalLM.from_pretrained("ddh0/EstopianOrcaMaid-13b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ddh0/EstopianOrcaMaid-13b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ddh0/EstopianOrcaMaid-13b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ddh0/EstopianOrcaMaid-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ddh0/EstopianOrcaMaid-13b

SGLang

How to use ddh0/EstopianOrcaMaid-13b 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 "ddh0/EstopianOrcaMaid-13b" \
    --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": "ddh0/EstopianOrcaMaid-13b",
		"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 "ddh0/EstopianOrcaMaid-13b" \
        --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": "ddh0/EstopianOrcaMaid-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ddh0/EstopianOrcaMaid-13b with Docker Model Runner:
```
docker model run hf.co/ddh0/EstopianOrcaMaid-13b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

EstopianOrcaMaid-13b

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

The goal of this merge is to create an unusually intelligent and human-like model especially for RP.

The prompt format is Alpaca. You can use the standard format as shown, but for best results, you should customize the system prompt to your specific needs.

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{YOUR MESSAGE HERE}

### Response:
{BOT MESSAGE HERE}

Misc. information

BOS token is <s>
EOS token is </s>
Native context length is 4096
Base model is Llama 2
Due to the inclusion of Orca-2-13b, the model is subject to the terms of the Microsoft Research License

Merge Method

This model was merged using the linear merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: /Volumes/Sabrent/LLMs/EstopianMaid-13B
    parameters:
      weight: 0.8
  - model: "/Volumes/SanDisk/LLM Archive/Orca-2-13b"
    parameters:
      weight: 0.2
merge_method: linear
dtype: float16