Instructions to use ddh0/OrcaMaidXL-17B-32k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ddh0/OrcaMaidXL-17B-32k with Transformers:

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

pipe = pipeline("text-generation", model="ddh0/OrcaMaidXL-17B-32k", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ddh0/OrcaMaidXL-17B-32k", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("ddh0/OrcaMaidXL-17B-32k", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ddh0/OrcaMaidXL-17B-32k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ddh0/OrcaMaidXL-17B-32k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ddh0/OrcaMaidXL-17B-32k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ddh0/OrcaMaidXL-17B-32k

SGLang

How to use ddh0/OrcaMaidXL-17B-32k 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/OrcaMaidXL-17B-32k" \
    --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/OrcaMaidXL-17B-32k",
		"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/OrcaMaidXL-17B-32k" \
        --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/OrcaMaidXL-17B-32k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ddh0/OrcaMaidXL-17B-32k with Docker Model Runner:
```
docker model run hf.co/ddh0/OrcaMaidXL-17B-32k
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

OrcaMaidXL-17B-32k

This is a a slightly experimental frankenmerge of Microsoft's Orca-2-13b and Undi and IkariDev's Noromaid-v0.2-13b.

The model recipe is as follows:

- [0, 12] from Noromaid (12 layers)
- [12, 22] from Orca (10 layers)
- [14, 24] from Orca (10 layers)
- [18, 28] from Orca (10 layers)
- [28, 40] from Noromaid (12 layers)

In my testing so far, the model performs exceptionally well. Your experience may vary.

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

Model size is 17.46B
BOS token is <s>
EOS token is </s>
Native context length is 32768 via YaRN (original context length was 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

Thanks

Thanks to Undi and IkariDev for Noromaid

Downloads last month: 129

Safetensors

Model size

17B params

Tensor type

F16

Model tree for ddh0/OrcaMaidXL-17B-32k

Quantizations

5 models