Open-Orca/OpenOrca
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How to use Ont/Marcoroni-13B with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Ont/Marcoroni-13B") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Ont/Marcoroni-13B")
model = AutoModelForCausalLM.from_pretrained("Ont/Marcoroni-13B")How to use Ont/Marcoroni-13B with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ont/Marcoroni-13B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Ont/Marcoroni-13B",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Ont/Marcoroni-13B
How to use Ont/Marcoroni-13B with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Ont/Marcoroni-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": "Ont/Marcoroni-13B",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "Ont/Marcoroni-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": "Ont/Marcoroni-13B",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Ont/Marcoroni-13B with Docker Model Runner:
docker model run hf.co/Ont/Marcoroni-13B
A conversion of the original model [AIDC-ai-business/Marcoroni-13B] to safetensors format.
### Instruction:
<prompt> (without the <>)
### Response:
| Metric | Value |
|---|---|
| Avg. | 65.76 |
| ARC (25-shot) | 62.46 |
| HellaSwag (10-shot) | 83.27 |
| MMLU (5-shot) | 59.63 |
| TruthfulQA (0-shot) | 57.7 |