Instructions to use abacusai/Smaug-Humanizer-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use abacusai/Smaug-Humanizer-GGUF 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 abacusai/Smaug-Humanizer-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf abacusai/Smaug-Humanizer-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf abacusai/Smaug-Humanizer-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf abacusai/Smaug-Humanizer-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 abacusai/Smaug-Humanizer-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf abacusai/Smaug-Humanizer-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 abacusai/Smaug-Humanizer-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf abacusai/Smaug-Humanizer-GGUF:Q4_K_M
Use Docker
docker model run hf.co/abacusai/Smaug-Humanizer-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use abacusai/Smaug-Humanizer-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "abacusai/Smaug-Humanizer-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abacusai/Smaug-Humanizer-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/abacusai/Smaug-Humanizer-GGUF:Q4_K_M
- Ollama
How to use abacusai/Smaug-Humanizer-GGUF with Ollama:
ollama run hf.co/abacusai/Smaug-Humanizer-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use abacusai/Smaug-Humanizer-GGUF with Docker Model Runner:
docker model run hf.co/abacusai/Smaug-Humanizer-GGUF:Q4_K_M
- Lemonade
How to use abacusai/Smaug-Humanizer-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull abacusai/Smaug-Humanizer-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Smaug-Humanizer-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Smaug Humanizer GGUF
GGUF files of abacusai/Smaug-Humanizer, a 12B model that rewrites AI-generated text so that it reads as if a person wrote it, while it keeps the facts, numbers and layout of the input. See the main model card for the training details.
Files
| File | Size | Use |
|---|---|---|
Smaug-Humanizer-BF16.gguf |
23.8 GB | full precision |
Smaug-Humanizer-Q8_0.gguf |
12.7 GB | near full quality, fits a 16 GB GPU |
Smaug-Humanizer-Q6_K.gguf |
9.8 GB | 12 GB GPUs |
Smaug-Humanizer-Q5_K_M.gguf |
8.5 GB | 10β12 GB GPUs |
Smaug-Humanizer-Q4_K_M.gguf |
7.4 GB | 8 GB GPUs and laptops |
Made with llama.cpp b11899 from the bf16 weights.
Prompt and sampling
Use one user turn with this prompt:
Rewrite the AI-written text below so that it reads as if a person wrote it, in a person's own voice, wording and rhythm.
Keep every fact, number, name and quote. Keep the structure and the language of the text. Output only the rewritten text.
Text:
{text}
Sampling: temperature 1.0, top_p 0.95, top_k 64.
Stop string: add <end_of_turn> as a stop string. The model ends its turn with this text, which is not a special token in the GGUF, so most clients will otherwise print it at the end of the answer.
For texts over about 500 words, split the input at paragraph breaks into chunks of about 500 words, rewrite each chunk, and join them with a blank line.
llama.cpp
Use llama.cpp b11899 or newer (Gemma 4 support). Download from Hugging Face and serve:
llama-server -hf abacusai/Smaug-Humanizer-GGUF:Q4_K_M -ngl 99 -c 8192 --port 8080
Or serve a file you already downloaded:
hf download abacusai/Smaug-Humanizer-GGUF Smaug-Humanizer-Q4_K_M.gguf --local-dir .
llama-server -m Smaug-Humanizer-Q4_K_M.gguf -ngl 99 -c 8192 --port 8080
-ngl 99 puts all layers on the GPU; leave it out to run on the CPU.
curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
"messages": [{"role": "user", "content": "<prompt above with your text>"}],
"temperature": 1.0, "top_p": 0.95, "top_k": 64, "stop": ["<end_of_turn>"]
}'
Ollama
ollama run hf.co/abacusai/Smaug-Humanizer-GGUF:Q4_K_M
Add PARAMETER stop "<end_of_turn>" to a Modelfile if the tag shows at the end of the answers.
LM Studio
Search for abacusai/Smaug-Humanizer-GGUF, pick a file, and add <end_of_turn> under Stop Strings in the model settings.
License
Apache 2.0, the same as abacusai/Smaug-Humanizer and its base model, gemma-4-12B. See LICENSE.
Citation
@misc{abacusai2026smaughumanizer,
title = {Smaug Humanizer},
author = {Abacus.AI},
year = {2026},
url = {https://huggingface.co/abacusai/Smaug-Humanizer}
}
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