AlekseyKorshuk/roleplay-io
Viewer • Updated • 3.15k • 125 • 24
How to use DevidCipher/RPG-Neuro with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="DevidCipher/RPG-Neuro") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("DevidCipher/RPG-Neuro")
model = AutoModelForCausalLM.from_pretrained("DevidCipher/RPG-Neuro")How to use DevidCipher/RPG-Neuro with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DevidCipher/RPG-Neuro"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "DevidCipher/RPG-Neuro",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/DevidCipher/RPG-Neuro
How to use DevidCipher/RPG-Neuro with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "DevidCipher/RPG-Neuro" \
--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": "DevidCipher/RPG-Neuro",
"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 "DevidCipher/RPG-Neuro" \
--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": "DevidCipher/RPG-Neuro",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use DevidCipher/RPG-Neuro with Docker Model Runner:
docker model run hf.co/DevidCipher/RPG-Neuro
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("DevidCipher/RPG-Neuro")
model = AutoModelForCausalLM.from_pretrained("DevidCipher/RPG-Neuro")A re-trained version of GPT-2 Large, made for fantasy, rpg and role-play text generation.
Some Metrics:
BLEU ~= 0,19
METEOR ~= 0,65
ROGUE ~= 0,41
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DevidCipher/RPG-Neuro")