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 / README.md

rty813

Add humanizer-bf16 weights (bf16)

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---
license: apache-2.0
base_model: jialinyyzz/humanizer
pipeline_tag: text-generation
library_name: transformers
tags:
- bf16
- humanizer
- full-precision
- transformers
- vllm
- xinference
---
# humanizer-bf16 (bf16)

Full-precision (bf16) PyTorch port of [jialinyyzz/humanizer](https://huggingface.co/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
---
<img src="assets/banner-en.png" alt="humanizer: rewrites AI drafts so they read like a person wrote them" width="100%">
<img src="assets/app-showcase-en.png" alt="The humanizer app running the 12B model locally: draft on the left, rewrite on the right, new wording highlighted" width="100%">
# 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)](https://github.com/sgaofen/humanizer-local-model/releases/download/app-v0.3.2/Humanizer-0.3.2-macos-arm64.dmg)** or **[Windows](https://github.com/sgaofen/humanizer-local-model/releases/download/app-v0.3.2/Humanizer-0.3.2-windows-x64-setup.exe)**, open it and paste a draft; it works offline. Without the app: [llama.cpp and other runtimes](#usage-without-the-app), or [AGENTS.md](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

	---
	license: apache-2.0
	base_model: jialinyyzz/humanizer
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- bf16
	- humanizer
	- full-precision
	- transformers
	- vllm
	- xinference
	---

	# humanizer-bf16 (bf16)

	Full-precision (bf16) PyTorch port of [jialinyyzz/humanizer](https://huggingface.co/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
	---

	<img src="assets/banner-en.png" alt="humanizer: rewrites AI drafts so they read like a person wrote them" width="100%">

	<img src="assets/app-showcase-en.png" alt="The humanizer app running the 12B model locally: draft on the left, rewrite on the right, new wording highlighted" width="100%">

	# 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)](https://github.com/sgaofen/humanizer-local-model/releases/download/app-v0.3.2/Humanizer-0.3.2-macos-arm64.dmg) or [Windows](https://github.com/sgaofen/humanizer-local-model/releases/download/app-v0.3.2/Humanizer-0.3.2-windows-x64-setup.exe), open it and paste a draft; it works offline. Without the app: [llama.cpp and other runtimes](#usage-without-the-app), or [AGENTS.md](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