Text Generation
Transformers
Safetensors
Japanese
mistral
causal-lm
Not-For-All-Audiences
nsfw
text-generation-inference
Instructions to use Elizezen/Nocturn-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Elizezen/Nocturn-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Elizezen/Nocturn-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Elizezen/Nocturn-7B") model = AutoModelForCausalLM.from_pretrained("Elizezen/Nocturn-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Elizezen/Nocturn-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Elizezen/Nocturn-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Elizezen/Nocturn-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Elizezen/Nocturn-7B
- SGLang
How to use Elizezen/Nocturn-7B 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 "Elizezen/Nocturn-7B" \ --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": "Elizezen/Nocturn-7B", "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 "Elizezen/Nocturn-7B" \ --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": "Elizezen/Nocturn-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Elizezen/Nocturn-7B with Docker Model Runner:
docker model run hf.co/Elizezen/Nocturn-7B
Nocturn 7B
Model Description
This is a 7B-parameter decoder-only Japanese language model fine-tuned on novel datasets, built on top of the base model Japanese Stable LM Base Gamma 7B. Japanese Stable LM Instruct Gamma 7B
Usage
Ensure you are using Transformers 4.34.0 or newer.
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Elizezen/Nocturn-7B")
model = AutoModelForCausalLM.from_pretrained(
"Elizezen/Nocturn-7B",
torch_dtype="auto",
)
model.eval()
if torch.cuda.is_available():
model = model.to("cuda")
input_ids = tokenizer.encode(
"吾輩は猫である。名前はまだない",,
add_special_tokens=True,
return_tensors="pt"
)
tokens = model.generate(
input_ids.to(device=model.device),
max_new_tokens=512,
temperature=1,
top_p=0.95,
do_sample=True,
)
out = tokenizer.decode(tokens[0][input_ids.shape[1]:], skip_special_tokens=True).strip()
print(out)
Intended Use
The model is mainly intended to be used for generating novels. It may not be so capable with instruction-based responses.
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