Instructions to use reeducator/bluemoonrp-30b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use reeducator/bluemoonrp-30b with Transformers:

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

pipe = pipeline("text-generation", model="reeducator/bluemoonrp-30b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("reeducator/bluemoonrp-30b")
model = AutoModelForCausalLM.from_pretrained("reeducator/bluemoonrp-30b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use reeducator/bluemoonrp-30b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "reeducator/bluemoonrp-30b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "reeducator/bluemoonrp-30b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/reeducator/bluemoonrp-30b

SGLang

How to use reeducator/bluemoonrp-30b 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 "reeducator/bluemoonrp-30b" \
    --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": "reeducator/bluemoonrp-30b",
		"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 "reeducator/bluemoonrp-30b" \
        --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": "reeducator/bluemoonrp-30b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use reeducator/bluemoonrp-30b with Docker Model Runner:
```
docker model run hf.co/reeducator/bluemoonrp-30b
```

General

Bluemoon roleplay finetune of LLaMA 33B (2 roleplayers only). This release also tests a longer 4k context token size achieved with AliBi.

Models

GGML 4-bit for llama.cpp

ggml-bluemoonrp-30b-4k-epoch6-q5_0.bin

GPTQ 4-bit CUDA:

bluemoonrp-30b-4k-epoch6-4bit-128g.safetensors

Remarks

This model has been trained using the following prompt (Vicuna 1.1 format):

A transcript of a roleplay between two players, LEAD and ASSOCIATE. LEAD sets up a scenario and the characters, from which ASSOCIATE then assumes a character role and continues the story for that role in response to description given by LEAD. The story and characters are developed by exchange of detailed event descriptions and character dialogs, successively given by both LEAD and ASSOCIATE.
LEAD: [role1 message]
ASSOCIATE: [role2 message]</s>

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bluemoonrp-30b

General

Models

Remarks

Dataset used to train reeducator/bluemoonrp-30b

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