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

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -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

Llama new
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

jialinyyzz commited on Oct 7

Commit

766072b

verified ·

1 Parent(s): c07c06c

App 0.3.2: import .docx/PDF and Create fact; download links to app-v0.3.2

Browse files

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README.md +1 -1

README.md CHANGED Viewed

@@ -36,7 +36,7 @@ tags:
 ## Quick start
-**App:** download the `.dmg` (Mac with Apple silicon) or the Windows installer from [Releases](https://github.com/sgaofen/humanizer-local-model/releases/latest) (current: [app 0.3.1](https://github.com/sgaofen/humanizer-local-model/releases/tag/app-v0.3.1)). On first run it suggests one of five sizes for your memory (Q8_0, Q6_K, Q4_K_M, Q3 or 2-bit, down to 8 GB machines; app 0.3.0 and later) and downloads it once; after that it works offline. Sizes and quality notes: [Quantized versions](#quantized-versions).
 **Command line, for long documents and agents:** `pipx install git+https://github.com/sgaofen/humanizer-local-model`, then `hz paper.md -o paper.out.md` (also `.txt` and `.docx`). It uses the app or a llama-server, keeps headings, code, tables and links, rewrites the prose piece by piece and flags any piece where a number went missing. See [USAGE.md, section 14](USAGE.md#14-hz-command-line-tool).

 ## Quick start
+**App:** download the `.dmg` (Mac with Apple silicon) or the Windows installer from [Releases](https://github.com/sgaofen/humanizer-local-model/releases/latest) (current: [app 0.3.2](https://github.com/sgaofen/humanizer-local-model/releases/tag/app-v0.3.2), which can import `.docx` and PDF files and keeps any passage you mark with **Create fact** word for word). On first run it suggests one of five sizes for your memory (Q8_0, Q6_K, Q4_K_M, Q3 or 2-bit, down to 8 GB machines; app 0.3.0 and later) and downloads it once; after that it works offline. Sizes and quality notes: [Quantized versions](#quantized-versions).
 **Command line, for long documents and agents:** `pipx install git+https://github.com/sgaofen/humanizer-local-model`, then `hz paper.md -o paper.out.md` (also `.txt` and `.docx`). It uses the app or a llama-server, keeps headings, code, tables and links, rewrites the prose piece by piece and flags any piece where a number went missing. See [USAGE.md, section 14](USAGE.md#14-hz-command-line-tool).