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

Libraries

How to use Fizzarolli/LayliticDolphinOpus with Transformers:

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

pipe = pipeline("text-generation", model="Fizzarolli/LayliticDolphinOpus")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Fizzarolli/LayliticDolphinOpus")
model = AutoModelForCausalLM.from_pretrained("Fizzarolli/LayliticDolphinOpus")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Fizzarolli/LayliticDolphinOpus with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Fizzarolli/LayliticDolphinOpus

SGLang

How to use Fizzarolli/LayliticDolphinOpus 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 "Fizzarolli/LayliticDolphinOpus" \
    --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": "Fizzarolli/LayliticDolphinOpus",
		"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 "Fizzarolli/LayliticDolphinOpus" \
        --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": "Fizzarolli/LayliticDolphinOpus",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

LayiticDolphinOpus

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

Notes

Hopping on the merge bandwagon, god save me from these names. Surprisingly this thing kinda works? It can (kinda) do assistant tasks, (kinda) do (E)RP. I still suck at this though

Recommended chat format is ChatML because all the source models use some variation of it, but honestly god knows what it'd work best with

Merge Details

Merge Method

This model was merged using the Model Stock merge method using alpindale/Mistral-7B-v0.2-hf 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: model_stock
base_model: alpindale/Mistral-7B-v0.2-hf
models:
    - model: dreamgen/opus-v1.2-7b
    - model: l3utterfly/mistral-7b-v0.2-layla-v4
    - model: cognitivecomputations/dolphin-2.8-mistral-7b-v02