Instructions to use KoboldAI/fairseq-dense-13B-Nerys-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KoboldAI/fairseq-dense-13B-Nerys-v2 with Transformers:

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

pipe = pipeline("text-generation", model="KoboldAI/fairseq-dense-13B-Nerys-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KoboldAI/fairseq-dense-13B-Nerys-v2")
model = AutoModelForCausalLM.from_pretrained("KoboldAI/fairseq-dense-13B-Nerys-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KoboldAI/fairseq-dense-13B-Nerys-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KoboldAI/fairseq-dense-13B-Nerys-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KoboldAI/fairseq-dense-13B-Nerys-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/KoboldAI/fairseq-dense-13B-Nerys-v2

SGLang

How to use KoboldAI/fairseq-dense-13B-Nerys-v2 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 "KoboldAI/fairseq-dense-13B-Nerys-v2" \
    --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": "KoboldAI/fairseq-dense-13B-Nerys-v2",
		"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 "KoboldAI/fairseq-dense-13B-Nerys-v2" \
        --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": "KoboldAI/fairseq-dense-13B-Nerys-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use KoboldAI/fairseq-dense-13B-Nerys-v2 with Docker Model Runner:
```
docker model run hf.co/KoboldAI/fairseq-dense-13B-Nerys-v2
```

Fairseq-dense 13B - Nerys

Model Description

Fairseq-dense 13B-Nerys is a finetune created using Fairseq's MoE dense model.

Training data

The training data contains around 2500 ebooks in various genres (the "Pike" dataset), a CYOA dataset called "CYS" and 50 Asian "Light Novels" (the "Manga-v1" dataset). Most parts of the dataset have been prepended using the following text: [Genre: <genre1>, <genre2>]

Revision changes:

Removed all headers
Removed all whitespaces
Some reshuffling of data

How to use

You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:

>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='KoboldAI/fairseq-dense-13B-Nerys')
>>> generator("Welcome Captain Janeway, I apologize for the delay.", do_sample=True, min_length=50)
[{'generated_text': 'Welcome Captain Janeway, I apologize for the delay."\nIt's all right," Janeway said. "I'm certain that you're doing your best to keep me informed of what\'s going on."'}]

Limitations and Biases

Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion).

BibTeX entry and citation info

Artetxe et al. (2021): Efficient Large Scale Language Modeling with Mixtures of Experts

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