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

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

#11

by Stickmouse - opened 22 days ago

Discussion

Stickmouse

22 days ago

Could we see a local HTTP link people could use with their existing model runner ex lm studios server?
Because I am running a dumb stack that limits me and the apps I use,

jialinyyzz

Owner 22 days ago

•

edited 22 days ago

You can already do this with LM Studio's own server. Load the GGUF in LM Studio (any file downloaded on or after Oct 4 has the chat template built in), start its local server, and point your app at its OpenAI-compatible endpoint (by default http://localhost:1234/v1). Send the draft as the only user message, one draft per request, with no system prompt; the template turns it into the exact prompt the model was trained on. Use temperature 1.0 and top-p 0.95. Setup steps are in the usage guide: https://github.com/sgaofen/humanizer-local-model/blob/main/docs/USAGE.md#8-lm-studio . The desktop app currently runs its own llama.cpp, so it can't plug into an existing server yet. Letting it use an external endpoint is a good idea, and I've noted it.

Stickmouse

22 days ago

Thanks for keeping it in mind because it looks good and it seems like a good app, and I would use it if it had been added

jialinyyzz

Owner 20 days ago

Ok, done. It'll be in the next app version (0.3.4). Go to the "…" menu, enable External model service, type your server's address (for LM Studio it's http://localhost:1234/v1), click Test connection, then Save. The app will then skip its own engine and send drafts to your server, with the same settings as USAGE.md section 8. Create fact and the language check still work. We've only tested it on a test server, so please report here if you see any issue with a real LM Studio.

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