Instructions to use athirdpath/Nethena-20b-Glued with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use athirdpath/Nethena-20b-Glued with Transformers:

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

pipe = pipeline("text-generation", model="athirdpath/Nethena-20b-Glued")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("athirdpath/Nethena-20b-Glued")
model = AutoModelForCausalLM.from_pretrained("athirdpath/Nethena-20b-Glued")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use athirdpath/Nethena-20b-Glued with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "athirdpath/Nethena-20b-Glued"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "athirdpath/Nethena-20b-Glued",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/athirdpath/Nethena-20b-Glued

SGLang

How to use athirdpath/Nethena-20b-Glued 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/Nethena-20b-Glued" \
    --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/Nethena-20b-Glued",
		"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/Nethena-20b-Glued" \
        --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/Nethena-20b-Glued",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use athirdpath/Nethena-20b-Glued with Docker Model Runner:
```
docker model run hf.co/athirdpath/Nethena-20b-Glued
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is NeverSleep/Nethena-20B with athirdpath/Nethena-20b-Glue-LORA applied.

athirdpath/Nethena-20b-Glue-LORA is a 128 rank LORA for RP, trained on a private dataset. It is unalligned and NSFW-oriented.

This is a test, exploring the effects of "gluing" the components of the 20b model together to reduce the iconic word replacement errors, increase lucidity, and improve recall.

The private ~500k token dataset used to train the LORA was Alpaca formatted and focused on 4 primary categories:

- Medical texts (on psychology, reproductive organs, anatomy, and pregnancy). These are formatted so the model, in character as a doctor or therapist, answers a patient's question in short to medium form.
- Excerpts from short stories and novellas (erotic and romantic) centered around both realistic and fantastic situations, covering several fetishes as well. These are sliced into ~2048 token chunks, and these long-form responses are all tied to the command “Enter narrator mode.” in the instructions.
- A selection from PIPPA, using a wide keyword search for tokens associated with low quality human or AI data to remove those responses, then a positive search was done for words and phrases associated with a higher reading level. These are converted to Alpaca with “Enter RP mode.” in all the instruction fields.
- ~18k tokens of GPT-4 generated data on role-playing from various characters’ perspectives, focusing on different situations and emotions. Includes many multi-turn conversations.

So far it is passing subjective testing with flying colors, objective numbers coming soon.

Trained with Alpaca-style prompts.

Downloads last month: 1,039

Model tree for athirdpath/Nethena-20b-Glued

Quantizations

3 models