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

Libraries

How to use athirdpath/Orca-2-13b-Alpaca-Uncensored-LORA with Transformers:

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

pipe = pipeline("text-generation", model="athirdpath/Orca-2-13b-Alpaca-Uncensored-LORA")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("athirdpath/Orca-2-13b-Alpaca-Uncensored-LORA")
model = AutoModelForCausalLM.from_pretrained("athirdpath/Orca-2-13b-Alpaca-Uncensored-LORA")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use athirdpath/Orca-2-13b-Alpaca-Uncensored-LORA with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/athirdpath/Orca-2-13b-Alpaca-Uncensored-LORA

SGLang

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

Docker Model Runner
How to use athirdpath/Orca-2-13b-Alpaca-Uncensored-LORA with Docker Model Runner:
```
docker model run hf.co/athirdpath/Orca-2-13b-Alpaca-Uncensored-LORA
```

qlora

This model is a fine-tuned version of microsoft/Orca-2-13b on a subset of the Vezora/Mini_Orca_Uncencored_Alpaca dataset, with some particularly spicy prompts added as well, to reduce the risk of rejections.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 6
eval_batch_size: 6
seed: 42
gradient_accumulation_steps: 6
total_train_batch_size: 36
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 15
num_epochs: 2

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