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

Libraries

How to use jialinyyzz/humanizer with Transformers:

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

pipe = pipeline("text-generation", model="jialinyyzz/humanizer")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("jialinyyzz/humanizer")
model = AutoModelForMultimodalLM.from_pretrained("jialinyyzz/humanizer", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use jialinyyzz/humanizer with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jialinyyzz/humanizer:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jialinyyzz/humanizer:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jialinyyzz/humanizer:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jialinyyzz/humanizer:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf jialinyyzz/humanizer:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf jialinyyzz/humanizer:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf jialinyyzz/humanizer:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jialinyyzz/humanizer:Q4_K_M

Use Docker

docker model run hf.co/jialinyyzz/humanizer:Q4_K_M

LM Studio
Jan

vLLM

How to use jialinyyzz/humanizer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jialinyyzz/humanizer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jialinyyzz/humanizer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jialinyyzz/humanizer:Q4_K_M

SGLang

How to use jialinyyzz/humanizer 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 "jialinyyzz/humanizer" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jialinyyzz/humanizer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "jialinyyzz/humanizer" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jialinyyzz/humanizer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use jialinyyzz/humanizer with Ollama:
```
ollama run hf.co/jialinyyzz/humanizer:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use jialinyyzz/humanizer with Docker Model Runner:
```
docker model run hf.co/jialinyyzz/humanizer:Q4_K_M
```

Lemonade

How to use jialinyyzz/humanizer with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jialinyyzz/humanizer:Q4_K_M

Run and chat with the model

lemonade run user.humanizer-Q4_K_M

List all available models

lemonade list

Atomic Chat

New discussion

Resources

View closed (3)

Speed on M5 Ultra: MLX 8-bit 69 tok/s, llama.cpp Q8_0 61, DSpark draft x1.3

🚀 1

#30 opened 3 days ago by

tristanbes

How to add other languages support?

#29 opened 4 days ago by

Gureumi

News on next gen?

#28 opened 4 days ago by

Grio43

eden

#27 opened 5 days ago by

FREISMUTH

Not good. Worse than gemma

➕ 2

#23 opened 9 days ago by

TheREZOR

effectivity

➕ 1

#22 opened 10 days ago by

SolsticeAI

vllm weights?

#21 opened 10 days ago by

huggyfaceenjoyer

Single handedly defeating an entire enterprise of AI detection software - Bravo

#20 opened 10 days ago by

rombodawg

checkout humanizer's speculative decoding model >> 50% increase of speed

#19 opened 11 days ago by

jayyun98

Usage of the word "stuff"

#18 opened 12 days ago by

mithulaartigala

Seems to work well with the original AI as "fact-checker".

#17 opened 12 days ago by

tm103

works on some ai detectors, but failed all on pangram

#15 opened 14 days ago by

Bnbdamp88

Looks good but

#14 opened 15 days ago by

Hypersniper

老哥你图啥？

#13 opened 16 days ago by

likewendy

Pangram?

#12 opened 16 days ago by

ItzPingCat

could we see a local http link people could use there existing model runner ex lm studios server

#11 opened 16 days ago by

Stickmouse

Installation Video and Testing - Step by Step

🔥 2

#9 opened 17 days ago by

fahdmirzac

watermarking

#8 opened 17 days ago by

Anoxiom

MTP or ngram head?

#7 opened 18 days ago by

TheREZOR

overall pretty good but easily detected

🔥 1

#6 opened 19 days ago by

slowmojoe231

An internal model shows better human style

🚀❤️ 6

#5 opened 19 days ago by

jialinyyzz

Overall really good

❤️ 1

#4 opened 20 days ago by

Grio43

Is is not what I expected

👀🤯 5

#3 opened 20 days ago by

ddonofrio

Consideration of fine-tuning on DiffusionGemma for faster inference?

#2 opened 21 days ago by

bandageshi

Start here (v2, updated Oct 9): app, file sizes, prompt format, what feedback helps

🔥 3

#1 opened about 1 month ago by

jialinyyzz