Instructions to use Elfrino/AndroidPrincess-20B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Elfrino/AndroidPrincess-20B with Transformers:

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

pipe = pipeline("text-generation", model="Elfrino/AndroidPrincess-20B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Elfrino/AndroidPrincess-20B")
model = AutoModelForCausalLM.from_pretrained("Elfrino/AndroidPrincess-20B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Elfrino/AndroidPrincess-20B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Elfrino/AndroidPrincess-20B

SGLang

How to use Elfrino/AndroidPrincess-20B 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 "Elfrino/AndroidPrincess-20B" \
    --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": "Elfrino/AndroidPrincess-20B",
		"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 "Elfrino/AndroidPrincess-20B" \
        --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": "Elfrino/AndroidPrincess-20B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

NOTES: LOOKS PROMISING SO FAR..... IN TESTING......*

Some findings: Articulate, long flowy sentences and can be pushed to high temps. Lacks the zany outlandish creativity of PsymedRP-20B or Xwin-MLewd-20B but it's still a solid story weaver..

##################################################################################################################

merge

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

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

NEXT TRIAL: make WizardLM into a 20B and SLERP it with XwinXtended? maybe....



slices:
  - sources:
      - model: seb-c/Psydestroyer-20B
        layer_range: [0, 62]
      - model: Elfrino/XwinXtended-20B
        layer_range: [0, 62]
merge_method: slerp
base_model: seb-c/Psydestroyer-20B
parameters:
  t:
    - filter: self_attn
      value: [0.8, 0.8, 0.9, 0.7, .8]
    - filter: mlp
      value: [.8, 0.8, 0.9, 0.8, .7]
    - value: 0.3369
dtype: bfloat16

Downloads last month: 6

Safetensors

Model size

20B params

Tensor type

BF16

Model tree for Elfrino/AndroidPrincess-20B

Elfrino/XwinXtended-20B

seb-c/Psydestroyer-20B

Merge model

this model

Quantizations

3 models