Text Generation
GGUF
Safetensors
Transformers
English
Chinese
gemma4_unified
image-text-to-text
humanizer
text-rewriting
rewriting
paraphrase
style-transfer
llama.cpp
gemma4
conversational
Instructions to use suryatmodulus/humanizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suryatmodulus/humanizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="suryatmodulus/humanizer")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("suryatmodulus/humanizer") model = AutoModelForMultimodalLM.from_pretrained("suryatmodulus/humanizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use suryatmodulus/humanizer 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 suryatmodulus/humanizer:Q4_K_M # Run inference directly in the terminal: llama cli -hf suryatmodulus/humanizer:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf suryatmodulus/humanizer:Q4_K_M # Run inference directly in the terminal: llama cli -hf suryatmodulus/humanizer: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 suryatmodulus/humanizer:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf suryatmodulus/humanizer: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 suryatmodulus/humanizer:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf suryatmodulus/humanizer:Q4_K_M
Use Docker
docker model run hf.co/suryatmodulus/humanizer:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use suryatmodulus/humanizer with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "suryatmodulus/humanizer" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "suryatmodulus/humanizer", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/suryatmodulus/humanizer:Q4_K_M
- SGLang
How to use suryatmodulus/humanizer 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 "suryatmodulus/humanizer" \ --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": "suryatmodulus/humanizer", "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 "suryatmodulus/humanizer" \ --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": "suryatmodulus/humanizer", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use suryatmodulus/humanizer with Ollama:
ollama run hf.co/suryatmodulus/humanizer:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use suryatmodulus/humanizer with Docker Model Runner:
docker model run hf.co/suryatmodulus/humanizer:Q4_K_M
- Lemonade
How to use suryatmodulus/humanizer with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull suryatmodulus/humanizer:Q4_K_M
Run and chat with the model
lemonade run user.humanizer-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 4,298 Bytes
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