Instructions to use ChuckMcSneed/Dicephal-123B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChuckMcSneed/Dicephal-123B with Transformers:

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

pipe = pipeline("text-generation", model="ChuckMcSneed/Dicephal-123B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ChuckMcSneed/Dicephal-123B")
model = AutoModelForCausalLM.from_pretrained("ChuckMcSneed/Dicephal-123B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ChuckMcSneed/Dicephal-123B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ChuckMcSneed/Dicephal-123B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ChuckMcSneed/Dicephal-123B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ChuckMcSneed/Dicephal-123B

SGLang

How to use ChuckMcSneed/Dicephal-123B 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 "ChuckMcSneed/Dicephal-123B" \
    --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": "ChuckMcSneed/Dicephal-123B",
		"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 "ChuckMcSneed/Dicephal-123B" \
        --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": "ChuckMcSneed/Dicephal-123B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ChuckMcSneed/Dicephal-123B with Docker Model Runner:
```
docker model run hf.co/ChuckMcSneed/Dicephal-123B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

I took base llama 2 70b model and frankenmerged it with itself using mergekit. Somehow it is coherent.

Thanks for featuring me at https://merge.moe/. I'll try my best to make even more good(!?) merges in the future.

Observations

It is more creative than the base model and has a sense of humor.
Just like Goliath is sometimes makes new words without meaning.
Just like the base model, it is quite disobedient, clever prompting is needed to get it to output answers.
Should be great for storywriting.
Significantly better than the base model in stylized writing and poems. Still far away from finetuned models.
The way it comes back at its past mistakes and fails my tests is almost human. (After model failed the test, I haven't told it yet that it failed) Me: "Why did you pick that?" Dicephal: "Because I am an idiot."

Benchmarks

NeoEvalPlusN_benchmark

My meme benchmark.

Test name	Base llama	Dicephal
B	0	0
C	2	0
D	0.5	1
S	1.25	2.25
P	0	2.25
Total	3.75	5.5

+75% in size, +47% in meme benchmark performance!

Politiscales test

Politiscales for llama

name	whacky	left/right
ChuckMcSneed/Dicephal-123B	1.742262578	-0.131433424
meta-llama/Llama-2-70b-hf	1.930293804	0.178771095

Downloads last month: 4

Safetensors

Model size

124B params

Tensor type

F16

Model tree for ChuckMcSneed/Dicephal-123B

Quantizations

2 models