Instructions to use ngquocvinh/Humanizer-12B-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 ngquocvinh/Humanizer-12B-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 ngquocvinh/Humanizer-12B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf ngquocvinh/Humanizer-12B-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 ngquocvinh/Humanizer-12B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf ngquocvinh/Humanizer-12B-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 ngquocvinh/Humanizer-12B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ngquocvinh/Humanizer-12B-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 ngquocvinh/Humanizer-12B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ngquocvinh/Humanizer-12B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/ngquocvinh/Humanizer-12B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use ngquocvinh/Humanizer-12B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ngquocvinh/Humanizer-12B-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": "ngquocvinh/Humanizer-12B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ngquocvinh/Humanizer-12B-GGUF:Q4_K_M
- Ollama
How to use ngquocvinh/Humanizer-12B-GGUF with Ollama:
ollama run hf.co/ngquocvinh/Humanizer-12B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use ngquocvinh/Humanizer-12B-GGUF with Docker Model Runner:
docker model run hf.co/ngquocvinh/Humanizer-12B-GGUF:Q4_K_M
- Lemonade
How to use ngquocvinh/Humanizer-12B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ngquocvinh/Humanizer-12B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Humanizer-12B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Humanizer 12B GGUF
Community GGUF quantizations of jialinyyzz/humanizer.
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I build and test these releases myself. Your coffee helps keep me going.
Thank you for supporting this work.
About Humanizer
Humanizer is an English and Chinese text-completion model for rewriting drafts. See the upstream model card for the model description and examples. Build the exact prompt as instr + "\n\n" + draft.strip() + sep and send it to /completion; the original prompt_format.json is included here. Upstream notes that GGUF chat templates are available but untested by the authors.
Artifact measurements
Sizes are measured from the local files in decimal GB (bytes / 1,000,000,000). Peak build RSS + cache is the observed cgroup estimate (process RSS plus charged file cache), in GiB. The BF16 row links to the upstream reference and was not quantized locally.
| File | Size (GB) | Peak build RSS + cache (GiB) |
|---|---|---|
| humanizer-12b-bf16.gguf (upstream reference) | 23.832050 | N/A |
humanizer-12b-Q8_0.gguf |
12.669631 | 12.156696 |
humanizer-12b-Q6_K.gguf |
9.786006 | 13.775974 |
humanizer-12b-Q5_K_M.gguf |
8.547252 | 12.997784 |
humanizer-12b-Q4_K_M.gguf |
7.381367 | 11.978336 |
humanizer-12b-Q3_K_M.gguf |
6.087072 | 10.795521 |
humanizer-12b-Q2_K.gguf |
4.830132 | 9.681194 |
humanizer-12b-IQ2_XS.gguf |
3.887827 | 8.917431 |
humanizer-12b-IQ1_M.gguf |
3.202833 | 14.173813 |
humanizer-12b-Q1_0.gguf |
2.377448 | 13.498875 |
humanizer-12b-Q5_K_S.gguf |
8.338356 | 18.925488 |
humanizer-12b-Q4_K_S.gguf |
7.024032 | 17.732090 |
humanizer-12b-Q3_K_S.gguf |
5.528214 | 16.260834 |
humanizer-12b-Q3_K_L.gguf |
6.566304 | 17.284103 |
humanizer-12b-IQ4_XS.gguf |
6.635240 | 17.493828 |
humanizer-12b-IQ3_XXS.gguf |
4.849179 | 15.862881 |
humanizer-12b-IQ2_XXS.gguf |
3.568093 | 14.703056 |
humanizer-12b-TQ2_0.gguf |
3.654785 | 14.486164 |
Peak build memory and file size describe artifact/build footprint, not inference speed or output fidelity. Each listed file passed a bilingual CPU smoke test with a non-empty generation for one English and one Chinese draft. No hold-out fidelity scores are published.
Quick start
Start llama.cpp with a published GGUF:
./llama-server \
-m humanizer-12b-Q8_0.gguf \
--host 127.0.0.1 --port 8080 --no-webui \
-ngl 0 -c 8192 -np 1
Humanizer is a text-completion model. For a reproducible request, build the prompt from the included prompt_format.json and use /completion. Upstream notes that GGUF chat templates are available but untested by the authors. This example submits an English draft:
import json
import requests
with open("prompt_format.json", encoding="utf-8") as f:
prompt_format = json.load(f)
draft = "The team moved the review to Friday because the shipment arrives on Thursday."
prompt = prompt_format["instr"] + "\n\n" + draft.strip() + prompt_format["sep"]
response = requests.post("http://127.0.0.1:8080/completion", json={
"prompt": prompt,
"n_predict": 2048,
"temperature": 1.0,
"top_p": 0.95,
"top_k": 0,
"min_p": 0.0,
"repeat_penalty": 1.0,
})
response.raise_for_status()
print(response.json()["content"])
For short English drafts, upstream notes that a Chinese reply can occasionally occur. If the output language is wrong, retry with the same settings up to three times.
Reproducibility and validation
- Artifact measurements report exact bytes and decimal GB.
- Build measurements report per-format peak RSS-plus-file-cache.
- Reproducibility manifest records the source revision, BF16 and prompt hashes, calibration split, imatrix, quantization commands, and runtime checks.
- SHA256 checksums cover the published GGUF files, prompt format, and license.
The quantizations are built directly from the pinned BF16 GGUF with this model's own imatrix. Build and runtime logs remain local and are not included here.
License and attribution
The upstream model is licensed under Apache-2.0; see LICENSE. These are community GGUF quantizations, not an official jialinyyzz release or endorsement.
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