Instructions to use messawey/orca_mini_v3_13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use messawey/orca_mini_v3_13b with Transformers:

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

pipe = pipeline("text-generation", model="messawey/orca_mini_v3_13b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("messawey/orca_mini_v3_13b")
model = AutoModelForCausalLM.from_pretrained("messawey/orca_mini_v3_13b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use messawey/orca_mini_v3_13b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "messawey/orca_mini_v3_13b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "messawey/orca_mini_v3_13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/messawey/orca_mini_v3_13b

SGLang

How to use messawey/orca_mini_v3_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 "messawey/orca_mini_v3_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": "messawey/orca_mini_v3_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 "messawey/orca_mini_v3_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": "messawey/orca_mini_v3_13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use messawey/orca_mini_v3_13b with Docker Model Runner:
```
docker model run hf.co/messawey/orca_mini_v3_13b
```

orca_mini_v3_13b / README.md

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	---
	license: apache-2.0
	tags:
	- merge
	- mergekit
	- lazymergekit
	- psmathur/orca_mini_v3_13b
	- garage-bAInd/Platypus2-13B
	- WizardLM/WizardMath-13B-V1.0
	---

	# psmathur/orca_mini_v3_13b

	psmathur/orca_mini_v3_13b is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
	* [psmathur/orca_mini_v3_13b](https://huggingface.co/psmathur/orca_mini_v3_13b)
	* [garage-bAInd/Platypus2-13B](https://huggingface.co/garage-bAInd/Platypus2-13B)
	* [WizardLM/WizardMath-13B-V1.0](https://huggingface.co/WizardLM/WizardMath-13B-V1.0)

	## 🧩 Configuration

	```yaml
	models:
	- model: psmathur/orca_mini_v3_13b
	parameters:
	density: [1, 0.7, 0.1] # density gradient
	weight: 1.0
	- model: garage-bAInd/Platypus2-13B
	parameters:
	density: 0.5
	weight: [0, 0.3, 0.7, 1] # weight gradient
	- model: WizardLM/WizardMath-13B-V1.0
	parameters:
	density: 0.33
	weight:
	- filter: mlp
	value: 0.5
	- value: 0
	merge_method: ties
	base_model: TheBloke/Llama-2-13B-fp16
	parameters:
	normalize: true
	int8_mask: true
	dtype: float16```