Instructions to use FallenMerick/Bionic-Vaquita-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FallenMerick/Bionic-Vaquita-13B with Transformers:

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

pipe = pipeline("text-generation", model="FallenMerick/Bionic-Vaquita-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FallenMerick/Bionic-Vaquita-13B")
model = AutoModelForCausalLM.from_pretrained("FallenMerick/Bionic-Vaquita-13B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FallenMerick/Bionic-Vaquita-13B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/FallenMerick/Bionic-Vaquita-13B

SGLang

How to use FallenMerick/Bionic-Vaquita-13B 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 "FallenMerick/Bionic-Vaquita-13B" \
    --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": "FallenMerick/Bionic-Vaquita-13B",
		"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 "FallenMerick/Bionic-Vaquita-13B" \
        --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": "FallenMerick/Bionic-Vaquita-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FallenMerick/Bionic-Vaquita-13B with Docker Model Runner:
```
docker model run hf.co/FallenMerick/Bionic-Vaquita-13B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Bionic-Vaquita-13B

In the same vein as the legendary Psyonic-Cetacean-20B, I have attempted to create a 13B model that is equal parts creative and chaotic, while still remaining coherent enough for roleplaying purposes.
Seven different Llama-2 13B models were hand-picked and merged via TIES to create three separate components for the final stack.
Emotional intelligence and coherency were the primary focus of the late-stage manual testing that led to selecting this model.

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

GGUF quants: https://huggingface.co/backyardai/Bionic-Vaquita-13B-GGUF

Merge Details

Merge Method

This model was merged using the passthrough merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
    - model: FallenMerick/XNoroChronos-Orca2-Noromaid
      layer_range: [0, 16]
  - sources:
    - model: FallenMerick/EstopianMaid-Orca2-MlewdBoros
      layer_range: [16, 24]
  - sources:
    - model: FallenMerick/Psyfighter2-Orca2-Erebus3
      layer_range: [24, 40]
merge_method: passthrough
dtype: bfloat16