Instructions to use ChuckMcSneed/PMaxxxer-v1-70b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChuckMcSneed/PMaxxxer-v1-70b with Transformers:

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

pipe = pipeline("text-generation", model="ChuckMcSneed/PMaxxxer-v1-70b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ChuckMcSneed/PMaxxxer-v1-70b")
model = AutoModelForCausalLM.from_pretrained("ChuckMcSneed/PMaxxxer-v1-70b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ChuckMcSneed/PMaxxxer-v1-70b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ChuckMcSneed/PMaxxxer-v1-70b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ChuckMcSneed/PMaxxxer-v1-70b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ChuckMcSneed/PMaxxxer-v1-70b

SGLang

How to use ChuckMcSneed/PMaxxxer-v1-70b 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/PMaxxxer-v1-70b" \
    --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/PMaxxxer-v1-70b",
		"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/PMaxxxer-v1-70b" \
        --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/PMaxxxer-v1-70b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ChuckMcSneed/PMaxxxer-v1-70b with Docker Model Runner:
```
docker model run hf.co/ChuckMcSneed/PMaxxxer-v1-70b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

BABE WAKE UP NEW MEME MODELS JUST DROPPED

Ladies and Gentlemen!

I present to you

drum roll

THE BENCHBREAKERS!

PMaxxxer (The Good)
SMaxxxer (The Bad)
BenchmaxxxerPS (The Ugly)

These three interesting models were designed in attempt to break my own meme benchmark and well... they failed. The results are interesting nontheless.

SMAXXXER

The aggressor, the angry and dumb hobo that will roleplay with you. This meme model was designed to break the stylized writing test, and it kinda did, still can't surpass ChatGPT though.

For its creation lzlv was TIES-merged with spicyboros, xwin and dolphin using mergekit.

PMAXXXER

The overly politically correct SJW university dropout, the failed writer that's not really good at anything. This meme model was designed to break the poems test and it's an absolute failure.

For its creation WinterGoddess was TIES-merged with euryale, xwin and dolphin using mergekit.

BENCHMAXXXER PS

The true meme model. Goliath-style frankenmerge of SMAXXXER and PMAXXXER. You might think: "Oh it's a frankenmerge, the characteristics of the models will even out, right?" This is completely wrong in this case, here characteristics of the models add up. You get an angry hobo stuck with an SJW in the same fucking body! It will assault you and then immediately apologize for it! Then it will assault you again! And apologize again! Kinda funny. It also has a bit different writing style compared to Goliath.

Is it worth using over Goliath? Not really. However, if you have fast internet and patience to try a 123b meme model, go for it!

FAILED MODELS(not gonna upload)

BENCHMAXXXER SP

Frankenmerge of SMAXXXER and PMAXXXER, just like BENCHMAXXXER PS, but in different order. Has severe brain damage, clearly the influence of the hobo is strong in this one.

BENCHMAXXXER SS

Self-merge of SMAXXXER, a bit less dumb and a bit less aggresive than the original SMAXXER.

BENCHMAXXXER MOE

2x70B MOE merge of SMAXXXER and PMAXXXER, unremarkable. Not smart, not angry. Just averaged out.

PROMPT FORMAT

Alpaca.

### Instruction:
{instruction}

### Input:
{input}

### Response:

Benchmarks

NeoEvalPlusN

My meme benchmark which the models were designed to break.

Test name	goliath-120b	PMaxxxer-v1-70b	SMaxxxer-v1-70b	BenchmaxxxerPS-v1-123b	BenchmaxxxerSP-v1-123b	BenchmaxxxerSS-v1-123b	BenchmaxxxerMOE-v1-123b
B	3	3	2	3	1.5	1.5	2
C	2	1	1	2	2	2	1
D	1	1	0	1	1	0.5	3
S	5	6.75	7.25	7.25	6.75	6.5	7.25
P	6	4.75	4.25	5.25	5.25	5.5	5
Total	17	16.5	14.5	18.5	16.5	16	18.25

Open LLM leaderboard

Leaderboard on Huggingface

Model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
PMaxxxer-v1-70b	72.41	71.08	87.88	70.39	59.77	82.64	62.7
SMaxxxer-v1-70b	72.23	70.65	88.02	70.55	60.7	82.87	60.58
Difference	0.18	0.43	-0.14	-0.16	-0.93	-0.23	2.12

Performance here is decent. It was #5 on the leaderboard among 70b models when I submitted it. This leaderboard is currently quite useless though, some 7b braindead meme merges have high scores there, claiming to be the next GPT4. At least I don't pretend that my models aren't a meme.

Downloads last month: 91

Safetensors

Model size

69B params

Tensor type

F16

Model tree for ChuckMcSneed/PMaxxxer-v1-70b

Quantizations

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Collection including ChuckMcSneed/PMaxxxer-v1-70b

Meme models

Collection

These models exist. Honk honk! • 13 items • Updated Apr 11, 2025