Instructions to use gabriellarson/Anonymizer-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gabriellarson/Anonymizer-4B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("gabriellarson/Anonymizer-4B-GGUF", dtype="auto")

llama-cpp-python

How to use gabriellarson/Anonymizer-4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="gabriellarson/Anonymizer-4B-GGUF",
	filename="Anonymizer-4B-F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use gabriellarson/Anonymizer-4B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf gabriellarson/Anonymizer-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf gabriellarson/Anonymizer-4B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf gabriellarson/Anonymizer-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf gabriellarson/Anonymizer-4B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf gabriellarson/Anonymizer-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf gabriellarson/Anonymizer-4B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf gabriellarson/Anonymizer-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gabriellarson/Anonymizer-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/gabriellarson/Anonymizer-4B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use gabriellarson/Anonymizer-4B-GGUF with Ollama:
```
ollama run hf.co/gabriellarson/Anonymizer-4B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use gabriellarson/Anonymizer-4B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for gabriellarson/Anonymizer-4B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for gabriellarson/Anonymizer-4B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for gabriellarson/Anonymizer-4B-GGUF to start chatting

Pi new

How to use gabriellarson/Anonymizer-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf gabriellarson/Anonymizer-4B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "gabriellarson/Anonymizer-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use gabriellarson/Anonymizer-4B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf gabriellarson/Anonymizer-4B-GGUF: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 gabriellarson/Anonymizer-4B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use gabriellarson/Anonymizer-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/gabriellarson/Anonymizer-4B-GGUF:Q4_K_M
```

Lemonade

How to use gabriellarson/Anonymizer-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gabriellarson/Anonymizer-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Anonymizer-4B-GGUF-Q4_K_M

List all available models

lemonade list

Model Card for eternisai/Anonymizer-4B

SLMs for semantically similar replacement of PII to provide better end-user privacy.

Model description

The Anonymizer-4B is the strongest model in the Enchanted anonymizer series. Effectively matching GPT-4.1 while being thousands of times smaller.

It is the most accurate variant available and powers advanced anonymization in Enchanted.

Intended use

Primary use: High-accuracy anonymizer inside Enchanted.
Secondary use: Deployments where top-quality anonymization is critical (enterprise, research).

Training details

Base: Qwen3-4B.
Data: ~30k samples covering PII replacement + non-replacement categories.
Method: Supervised fine-tuning → GRPO with GPT-4.1 as judge.
Score: 9.55/10 on anonymization quality.
Latency: <250ms TTFT, <2s full completion (quantized).

Limitations

Largest model in the series, not suitable for mobile inference as of August 2025.
Requires MacBook-class hardware or above for real-time use.

Usage example

<tool_call>
{"name": "replace_entities", "arguments": {"replacements": [
  {"original": "Marc", "replacement": "Robert"},
  {"original": "cloud infrastructure", "replacement": "enterprise software"}
]}}
</tool_call>

Usage prompt template

The models expect input in this specific format:

[BEGIN OF TASK INSTRUCTION]
You are an anonymizer. Your task is to identify and replace personally identifiable information (PII) in the given text.
Replace PII entities with semantically equivalent alternatives that preserve the context needed for a good response.
If no PII is found or replacement is not needed, return an empty replacements list.
REPLACEMENT RULES:
• Personal names: Replace private or small-group individuals. Pick same culture + gender + era; keep surnames aligned across family members. DO NOT replace globally recognised public figures (heads of state, Nobel laureates, A-list entertainers, Fortune-500 CEOs, etc.).
• Companies / organisations: Replace private, niche, employer & partner orgs. Invent a fictitious org in the same industry & size tier; keep legal suffix. Keep major public companies (anonymity set ≥ 1,000,000).
• Projects / codenames / internal tools: Always replace with a neutral two-word alias of similar length.
• Locations: Replace street addresses, buildings, villages & towns < 100k pop with a same-level synthetic location inside the same state/country. Keep big cities (≥ 1M), states, provinces, countries, iconic landmarks.
• Dates & times: Replace birthdays, meeting invites, exact timestamps. Shift day/month by small amounts while KEEPING THE SAME YEAR to maintain temporal context. DO NOT shift public holidays or famous historic dates ("July 4 1776", "Christmas Day", "9/11/2001", etc.). Keep years, fiscal quarters, decade references unchanged.
• Identifiers: (emails, phone #s, IDs, URLs, account #s) Always replace with format-valid dummies; keep domain class (.com big-tech, .edu, .gov).
• Monetary values: Replace personal income, invoices, bids by × [0.8 – 1.25] to keep order-of-magnitude. Keep public list prices & market caps.
• Quotes / text snippets: If the quote contains PII, swap only the embedded tokens; keep the rest verbatim.
[END OF TASK INSTRUCTION]
[BEGIN OF AVAILABLE TOOLS]
[{"type": "function", "function": {"name": "replace_entities", "description": "Replace PII entities with anonymized versions", "parameters": {"type": "object", "properties": {"replacements": {"type": "array", "items": {"type": "object", "properties": {"original": {"type": "string"}, "replacement": {"type": "string"}}, "required": ["original", "replacement"]}}}, "required": ["replacements"]}}}]
[END OF AVAILABLE TOOLS]
[BEGIN OF FORMAT INSTRUCTION]
Use the replace_entities tool to specify replacements. Your response must use the tool call wrapper format:
<|tool_call|>{"name": "replace_entities", "arguments": {"replacements": [{"original": "PII_TEXT", "replacement": "ANONYMIZED_TEXT"}, ...]}}</|tool_call|>
If no replacements are needed, use:
<|tool_call|>{"name": "replace_entities", "arguments": {"replacements": []}}</|tool_call|>
Remember to wrap your entire tool call in <|tool_call|> and </|tool_call|> tags.
[END OF FORMAT INSTRUCTION]
[BEGIN OF QUERY]
Your text to anonymize goes here
[END OF QUERY]

Downloads last month: 112

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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Model tree for gabriellarson/Anonymizer-4B-GGUF

Base model

eternisai/Anonymizer-4B

Quantized

(5)

this model