Instructions to use contextboxai/vi-humanizer-gemma-4-e2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use contextboxai/vi-humanizer-gemma-4-e2b with Transformers:

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

pipe = pipeline("text-generation", model="contextboxai/vi-humanizer-gemma-4-e2b")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

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

processor = AutoProcessor.from_pretrained("contextboxai/vi-humanizer-gemma-4-e2b")
model = AutoModelForMultimodalLM.from_pretrained("contextboxai/vi-humanizer-gemma-4-e2b", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use contextboxai/vi-humanizer-gemma-4-e2b 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 contextboxai/vi-humanizer-gemma-4-e2b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf contextboxai/vi-humanizer-gemma-4-e2b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf contextboxai/vi-humanizer-gemma-4-e2b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf contextboxai/vi-humanizer-gemma-4-e2b: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 contextboxai/vi-humanizer-gemma-4-e2b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf contextboxai/vi-humanizer-gemma-4-e2b:Q4_K_M

Use Docker

docker model run hf.co/contextboxai/vi-humanizer-gemma-4-e2b:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use contextboxai/vi-humanizer-gemma-4-e2b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "contextboxai/vi-humanizer-gemma-4-e2b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "contextboxai/vi-humanizer-gemma-4-e2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/contextboxai/vi-humanizer-gemma-4-e2b:Q4_K_M

SGLang

How to use contextboxai/vi-humanizer-gemma-4-e2b 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 "contextboxai/vi-humanizer-gemma-4-e2b" \
    --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": "contextboxai/vi-humanizer-gemma-4-e2b",
		"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 "contextboxai/vi-humanizer-gemma-4-e2b" \
        --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": "contextboxai/vi-humanizer-gemma-4-e2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use contextboxai/vi-humanizer-gemma-4-e2b with Ollama:
```
ollama run hf.co/contextboxai/vi-humanizer-gemma-4-e2b:Q4_K_M
```
Unsloth Desktop

How to use contextboxai/vi-humanizer-gemma-4-e2b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf contextboxai/vi-humanizer-gemma-4-e2b:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "contextboxai/vi-humanizer-gemma-4-e2b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use contextboxai/vi-humanizer-gemma-4-e2b with Docker Model Runner:
```
docker model run hf.co/contextboxai/vi-humanizer-gemma-4-e2b:Q4_K_M
```

Lemonade

How to use contextboxai/vi-humanizer-gemma-4-e2b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull contextboxai/vi-humanizer-gemma-4-e2b:Q4_K_M

Run and chat with the model

lemonade run user.vi-humanizer-gemma-4-e2b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use contextboxai/vi-humanizer-gemma-4-e2b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf contextboxai/vi-humanizer-gemma-4-e2b:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default contextboxai/vi-humanizer-gemma-4-e2b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use contextboxai/vi-humanizer-gemma-4-e2b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf contextboxai/vi-humanizer-gemma-4-e2b:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "contextboxai/vi-humanizer-gemma-4-e2b:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

vi-humanizer · Gemma 4 E2B

A small assistant whose Vietnamese reads as if a native speaker wrote it. Ask it anything (explain, advise, plan, write an email or a post, translate), or give it an AI-written draft to rewrite. Answers come with the right tone and xưng hô, without chatbot preambles, closing offers or translationese.

About 2B effective parameters, built on Gemma 4 E2B. Weights in bf16 for transformers and vLLM, and GGUF files for llama.cpp, LM Studio and Ollama, down to 3.4 GB.

Quick start

Local app (offline): ContextBoxAI/vi-humanizer is a web app with a rewrite view (draft and rewrite side by side, with the changes and a numbers check) and a chat view.

git clone https://github.com/ContextBoxAI/vi-humanizer && cd vi-humanizer
./install.sh          # macOS, Linux; .\install.ps1 on Windows

It installs llama.cpp if needed, downloads the Q6_K file once and opens the app in your browser.

llama.cpp:

llama-server -hf contextboxai/vi-humanizer-gemma-4-e2b:Q6_K --jinja -ngl 99    # OpenAI-compatible API on :8080

The GGUF files carry their own chat template and sampling defaults (temperature 1.0, top-p 0.95, top-k 64), so no other flags are needed. LM Studio picks them up too. For Ollama, use the Modelfile from the app repo.

vLLM: vllm serve contextboxai/vi-humanizer-gemma-4-e2b

transformers:

from transformers import AutoTokenizer, Gemma4ForConditionalGeneration
tok = AutoTokenizer.from_pretrained("contextboxai/vi-humanizer-gemma-4-e2b")
model = Gemma4ForConditionalGeneration.from_pretrained("contextboxai/vi-humanizer-gemma-4-e2b", dtype="bfloat16", device_map="auto")
msgs = [{"role": "user", "content": "Viết giúp mình tin nhắn xin sếp nghỉ chiều nay để đưa con đi khám."}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)
print(tok.decode(model.generate(**ids, max_new_tokens=512, do_sample=True)[0][ids["input_ids"].shape[1]:], skip_special_tokens=True))

No system prompt is needed; one you add (an operator role, a persona) is respected. Thinking mode is off in the chat template.

Files

File	Size	Use
`model.safetensors` + configs	10.2 GB	transformers, vLLM
`vi-humanizer-e2b-bf16.gguf`	9.3 GB	llama.cpp, unquantised
`vi-humanizer-e2b-Q8_0.gguf`	4.9 GB	llama.cpp
`vi-humanizer-e2b-Q6_K.gguf`	3.8 GB	llama.cpp, recommended
`vi-humanizer-e2b-Q4_K_M.gguf`	3.4 GB	llama.cpp, smallest
`imatrix.gguf`	2.8 MB	importance matrix used for Q4_K_M

License

A derivative of Gemma, subject to the Gemma Terms of Use.

Downloads last month: 1,010

Safetensors

Model size

5B params

Tensor type

BF16

Model tree for contextboxai/vi-humanizer-gemma-4-e2b

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Quantized

(440)

this model

Quantizations

2 models