Instructions to use ResplendentAI/Asherah_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ResplendentAI/Asherah_7B with Transformers:

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

pipe = pipeline("text-generation", model="ResplendentAI/Asherah_7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ResplendentAI/Asherah_7B")
model = AutoModelForCausalLM.from_pretrained("ResplendentAI/Asherah_7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ResplendentAI/Asherah_7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ResplendentAI/Asherah_7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ResplendentAI/Asherah_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ResplendentAI/Asherah_7B

SGLang

How to use ResplendentAI/Asherah_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 "ResplendentAI/Asherah_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": "ResplendentAI/Asherah_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 "ResplendentAI/Asherah_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": "ResplendentAI/Asherah_7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ResplendentAI/Asherah_7B with Docker Model Runner:
```
docker model run hf.co/ResplendentAI/Asherah_7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Asherah

GGUF here: https://huggingface.co/Lewdiculous/Asherah_7B-GGUF-IQ-Imatrix

Asherah, goddess of all creation according to ancient myth was a huge inspiration for this model. The model started with a merge of four of Sanji Watsuki's models using various methods. This merge was then finetuned on Gnosis and Synthetic Soul, two datasets penned by myself.

You can use this as mmproj: https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/blob/main/mmproj-model-f16.gguf

I have also included a folder in the repo containing this file. It will be necessary for multimodal GGUF users. I recommend Koboldcpp.

Multimodal functionality is limited to GGUF users at this time. You can still use this model as a standard LLM.