Instructions to use rty813/humanizer-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rty813/humanizer-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="rty813/humanizer-bf16")

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

processor = AutoProcessor.from_pretrained("rty813/humanizer-bf16")
model = AutoModelForMultimodalLM.from_pretrained("rty813/humanizer-bf16", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rty813/humanizer-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rty813/humanizer-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rty813/humanizer-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rty813/humanizer-bf16

SGLang

How to use rty813/humanizer-bf16 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 "rty813/humanizer-bf16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rty813/humanizer-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "rty813/humanizer-bf16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rty813/humanizer-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rty813/humanizer-bf16 with Docker Model Runner:
```
docker model run hf.co/rty813/humanizer-bf16
```

humanizer-bf16 (bf16)

Full-precision (bf16) PyTorch port of jialinyyzz/humanizer, for vLLM / TGI / Xinference / transformers users who don't want the GGUF quant.

Format: pytorch_model.bin (bf16). safetensors conversion in progress.
Architecture: identical to the original — drop-in for AutoModelForCausalLM.from_pretrained("rty813/humanizer-bf16").
Also loads in vLLM (vllm serve rty813/humanizer-bf16) and Xinference (search "humanizer").

Original model card:

license: apache-2.0 base_model: google/gemma-4-12B base_model_relation: finetune language: - en - zh pipeline_tag: text-generation library_name: gguf tags: - humanizer - transformers - safetensors - text-rewriting - rewriting - paraphrase - style-transfer - gguf - llama.cpp - gemma4

humanizer

A 12B model that rewrites AI-written drafts (emails, essays, reports, forum posts; English and Chinese) so they read like a person wrote them. It is trained to keep every number, unit, date, name and quote and to add nothing, and it runs locally.

Quick start: get the app for Mac (Apple silicon) or Windows, open it and paste a draft; it works offline. Without the app: llama.cpp and other runtimes, or AGENTS.md for coding agents.

Before and after

Drafts from the held-out evaluation set. For each we generated 8 samples with the Q8_0 file (llama.cpp, the app's settings) and picked the one that reads best

Downloads last month: -

Model tree for rty813/humanizer-bf16

Base model

google/gemma-4-12B

Finetuned

jialinyyzz/humanizer

Finetuned

(1)

this model