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

Looks good but

#14

by Hypersniper - opened about 5 hours ago

Discussion

Hypersniper

about 5 hours ago

I've been using Gemma4 mostly. Is this any better? I tested a few examples with Gemma4, and here is one it produced:

Orginal:
Hi Maya,

Thank you again for joining our software demo on January 20. I appreciated the thoughtful questions from you and the Northstar Operations team, especially around user permissions, reporting, and integration with Salesforce.

I wanted to follow up and see whether your team needs a detailed pricing breakdown as you evaluate next steps. We can provide costs by license type, implementation services, training, and optional add-ons, along with estimates for 25, 50, or 100 users. I’d also be happy to outline the differences between our Professional and Enterprise plans.

If helpful, I can send the breakdown by Friday, January 24, or schedule a brief 20-minute call to walk through it. Please let me know what would be most useful as you prepare for your February budget review.

Best,
Jordan Lee

Converted:
Hi Maya,

Thanks again for joining our software demo on January 20. I really liked the questions from you and the Northstar Operations team, especially about user permissions, reporting, and Salesforce integration.

Just following up to see if your team needs a detailed pricing breakdown as you evaluate next steps. We can send over costs by license type, implementation services, training, and optional add-ons, along with estimates for 25, 50, or 100 users. I can also outline the differences between our Professional and Enterprise plans.

If that helps, I can send the breakdown by Friday, January 24, or schedule a brief 20-minute call to walk through it. Let me know what would be most useful as you prepare for your February budget review.

Best,
Jordan Lee

Prompt:
Keep the sentences; just remove or add words like Grammarly that do not make it sound like an AI has written it so they read as if a person wrote them. Highlight the changed parts in bold.

jialinyyzz

Owner about 4 hours ago

Thanks for testing! One thing to check: the output above looks like it came from Gemma 4 with your own prompt, not from humanizer. That prompt asks it to keep the sentences and only swap a few words, so a light, Grammarly-style edit is what you'd expect.

humanizer is a separate fine-tune and doesn't take custom instructions. Paste only the draft into the Space or the app, or send the exact wrapper from prompt_format.json (temperature 1.0, top_p 0.95). It usually changes much more: sentences get split, merged and reordered, while the names, dates and numbers stay.

Sampling is random, so once in a while the first rewrite stays close to the draft. If that happens, just run it again. If it still looks light on this email, post the output and say which file or app version you used, and I'll take a look.

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