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 Sep 11

Commit

2e8087e

verified ·

1 Parent(s): 0335dae

model card: Q6_K released, Q5_K_M withheld

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -16,7 +16,7 @@ library_name: transformers
 # humanizer-gemma-4-e4b
-One repo, every variant: transformers bf16 at the root; llama.cpp GGUF (Q8_0 / bf16; Q5_K_M and Q6_K pending evaluation, Q4_K_M withheld because 4-bit degenerates on this model) under `gguf/`. Samples, code and the evaluation set: [github.com/sgaofen/humanizer](https://github.com/sgaofen/humanizer).
 Rewrites AI-written drafts so they read like a person wrote them, keeping every fact, number, name and date. Fine-tune of `google/gemma-4-E4B`.
@@ -37,7 +37,7 @@ Rewrites AI-written drafts so they read like a person wrote them, keeping every
 | Chinese cases passing judge | 10 / 16 | 4 / 16 |
 | Originality.ai "human" *(external check only)* | 79 % | 57 % |
-GGUF files (Q8_0 / bf16; Q5_K_M and Q6_K pending evaluation, Q4_K_M withheld — 53/62 critical errors, gibberish) are in the `gguf/` folder of this repo; their quality on the same set is in `docs/QUALITY.md` of the GitHub repo. MLX 4-/6-bit quantisation of this model is not usable (Gemma 4 PLE layers); use the GGUF quants on a Mac.
 Known failure modes: ~1 in 10 outputs has a meaning flip (who did what, ordered vs received, a metric renamed); subject lines/greetings occasionally dropped; Chinese weaker than English; short drafts (< 120 words) less reliable. **Proofread numbers, dates and the direction of every claim.**

 # humanizer-gemma-4-e4b
+One repo, every variant: transformers bf16 at the root; llama.cpp GGUF (Q8_0 / Q6_K / bf16; Q5_K_M and Q4_K_M withheld — fidelity collapses below 6-bit on this model) under `gguf/`. Samples, code and the evaluation set: [github.com/sgaofen/humanizer](https://github.com/sgaofen/humanizer).
 Rewrites AI-written drafts so they read like a person wrote them, keeping every fact, number, name and date. Fine-tune of `google/gemma-4-E4B`.
 | Chinese cases passing judge | 10 / 16 | 4 / 16 |
 | Originality.ai "human" *(external check only)* | 79 % | 57 % |
+GGUF files (Q8_0 / Q6_K / bf16; Q5_K_M withheld at 17/62 critical errors, Q4_K_M withheld as gibberish) are in the `gguf/` folder of this repo; their quality on the same set is in `docs/QUALITY.md` of the GitHub repo. MLX 4-/6-bit quantisation of this model is not usable (Gemma 4 PLE layers); use the GGUF quants on a Mac.
 Known failure modes: ~1 in 10 outputs has a meaning flip (who did what, ordered vs received, a metric renamed); subject lines/greetings occasionally dropped; Chinese weaker than English; short drafts (< 120 words) less reliable. **Proofread numbers, dates and the direction of every claim.**