Instructions to use jeiku/NarrativeNexus_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jeiku/NarrativeNexus_7B with Transformers:

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

pipe = pipeline("text-generation", model="jeiku/NarrativeNexus_7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jeiku/NarrativeNexus_7B")
model = AutoModelForCausalLM.from_pretrained("jeiku/NarrativeNexus_7B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jeiku/NarrativeNexus_7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jeiku/NarrativeNexus_7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jeiku/NarrativeNexus_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jeiku/NarrativeNexus_7B

SGLang

How to use jeiku/NarrativeNexus_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 "jeiku/NarrativeNexus_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": "jeiku/NarrativeNexus_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 "jeiku/NarrativeNexus_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": "jeiku/NarrativeNexus_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jeiku/NarrativeNexus_7B with Docker Model Runner:
```
docker model run hf.co/jeiku/NarrativeNexus_7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Nexus

This is my new favorite 7B, made from a merge of tunes and merges that I've tossed together over the last week or so. This model seems to be greater than the sum of its parts, and is performing well in riddle testing and markdown role playing. I have also been using this model to generate 1000 token narratives that I am using to improve custom story datasets for use with future models. It is highly descriptive and readily fills a futanari character. You can likely utilize it for female or male characters as well. Enjoy!

GGUF here: https://huggingface.co/jeiku/NarrativeNexus_7B_GGUF

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using jeiku/Cookie_7B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: dare_ties
base_model: jeiku/Cookie_7B
parameters:
  normalize: true
models:
  - model: jeiku/SpaghettiOs_7B
    parameters:
      weight: 1
  - model: jeiku/Rainbow_69_7B
    parameters:
      weight: 1
  - model: jeiku/Paranoid_Android_7B
    parameters:
      weight: 0.75            
dtype: float16