Instructions to use Locutusque/Orca-2-13b-SFT-v6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Locutusque/Orca-2-13b-SFT-v6 with Transformers:

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

pipe = pipeline("text-generation", model="Locutusque/Orca-2-13b-SFT-v6")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Locutusque/Orca-2-13b-SFT-v6")
model = AutoModelForMultimodalLM.from_pretrained("Locutusque/Orca-2-13b-SFT-v6")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Locutusque/Orca-2-13b-SFT-v6 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-v6"
# 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-v6",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Locutusque/Orca-2-13b-SFT-v6

SGLang

How to use Locutusque/Orca-2-13b-SFT-v6 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-v6" \
    --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-v6",
		"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-v6" \
        --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-v6",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Locutusque/Orca-2-13b-SFT-v6 with Docker Model Runner:
```
docker model run hf.co/Locutusque/Orca-2-13b-SFT-v6
```
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, LDJnr/Capybara, LDJnr/Pure-Dove, LDJnr/LessWrong-Amplify-Instruct, LDJnr/Verified-Camel, mlabonne/guanaco-llama2-1k, and OpenAssistant/oasst_top1_2023-08-25. This model achieved a test loss of 0.39 on LDJnr/Verified-Camel.

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.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	56.15
AI2 Reasoning Challenge (25-Shot)	60.41
HellaSwag (10-Shot)	80.46
MMLU (5-Shot)	59.51
TruthfulQA (0-shot)	54.01
Winogrande (5-shot)	77.43
GSM8k (5-shot)	5.08

Downloads last month: 9

Safetensors

Model size

13B params

Tensor type

BF16

Model tree for Locutusque/Orca-2-13b-SFT-v6

Base model

microsoft/Orca-2-13b

Finetuned

(5)

this model

Quantizations

3 models

Datasets used to train Locutusque/Orca-2-13b-SFT-v6

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

60.410
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

80.460
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

59.510
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

54.010
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

77.430
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

5.080