Instructions to use Locutusque/Orca-2-13b-SFT_v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Locutusque/Orca-2-13b-SFT_v5 with Transformers:

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

pipe = pipeline("text-generation", model="Locutusque/Orca-2-13b-SFT_v5")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Locutusque/Orca-2-13b-SFT_v5")
model = AutoModelForCausalLM.from_pretrained("Locutusque/Orca-2-13b-SFT_v5")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Locutusque/Orca-2-13b-SFT_v5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Locutusque/Orca-2-13b-SFT_v5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Locutusque/Orca-2-13b-SFT_v5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Locutusque/Orca-2-13b-SFT_v5

SGLang

How to use Locutusque/Orca-2-13b-SFT_v5 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 "Locutusque/Orca-2-13b-SFT_v5" \
    --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": "Locutusque/Orca-2-13b-SFT_v5",
		"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 "Locutusque/Orca-2-13b-SFT_v5" \
        --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": "Locutusque/Orca-2-13b-SFT_v5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Locutusque/Orca-2-13b-SFT_v5 with Docker Model Runner:
```
docker model run hf.co/Locutusque/Orca-2-13b-SFT_v5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

The "microsoft/Orca-2-13b" model fully fine-tuned on HuggingFaceH4/no_robots, totally-not-an-llm/EverythingLM-data-V3, mlabonne/guanaco-llama2-1k, OpenAssistant/oasst_top1_2023-08-25, and garage-bAInd/Open-Platypus. This model achieved a test loss of 0.38 on garage-bAInd/Open-Platypus.

Make sure to comply with the microsoft research license. Please read it before using this model.

This model was trained on the ChatML prompt template.

The responses seen in the inference API were generated using the following sampling parameters:

temperature = 0.1

top_p = 0.14

top_k = 41

repetition_penalty = 1.176

Downloads last month: 12

Safetensors

Model size

13B params

Tensor type

BF16

Model tree for Locutusque/Orca-2-13b-SFT_v5

Base model

microsoft/Orca-2-13b

Finetuned

(5)

this model

Quantizations

4 models

Locutusque
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Orca-2-13b-SFT_v5

Model tree for Locutusque/Orca-2-13b-SFT_v5

Datasets used to train Locutusque/Orca-2-13b-SFT_v5