Instructions to use Dans-Archive/Dans-QuestionableCocktail-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dans-Archive/Dans-QuestionableCocktail-13b with Transformers:

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

pipe = pipeline("text-generation", model="Dans-Archive/Dans-QuestionableCocktail-13b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Dans-Archive/Dans-QuestionableCocktail-13b")
model = AutoModelForCausalLM.from_pretrained("Dans-Archive/Dans-QuestionableCocktail-13b", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Dans-Archive/Dans-QuestionableCocktail-13b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Dans-Archive/Dans-QuestionableCocktail-13b

SGLang

How to use Dans-Archive/Dans-QuestionableCocktail-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 "Dans-Archive/Dans-QuestionableCocktail-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": "Dans-Archive/Dans-QuestionableCocktail-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 "Dans-Archive/Dans-QuestionableCocktail-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": "Dans-Archive/Dans-QuestionableCocktail-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Dans-Archive/Dans-QuestionableCocktail-13b with Docker Model Runner:
```
docker model run hf.co/Dans-Archive/Dans-QuestionableCocktail-13b
```

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

This is my laziest model card to date, its a merge of about 70% Llama-2-chat, 15% Holodeck, and 15% Chronos.

My goals with this were to break induce some prose and novel structuring into Llama-2-chat without losing its magic. I think it worked? idk this probably wont be the last of this series.

Holodeck and Chronos were merged 50/50 into 'HoloChronos' then layer only merged with the following pattern into Llama-2-chat with 'HoloChronos' being the #2 model

[0.3, 0.2, 0.2, 0.2, 0.3, 0.5, 0.4, 0.2, 0.3, 0.4]

I recommend using the Llama-2-chat prompt format but model merges are unholy so YMMV.

Thank you to Gryphe for his merge script https://github.com/Gryphe/BlockMerge_Gradient/tree/main

Thank you to Mr. Seeker and Elinas for the Holodeck and ChronosV2 respectively.

https://huggingface.co/KoboldAI/LLAMA2-13B-Holodeck-1

https://huggingface.co/elinas/chronos-13b-v2

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