Instructions to use matthewagi/re-write-assist-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use matthewagi/re-write-assist-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="matthewagi/re-write-assist-gguf",
	filename="re-write-assist-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use matthewagi/re-write-assist-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 matthewagi/re-write-assist-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf matthewagi/re-write-assist-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 matthewagi/re-write-assist-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf matthewagi/re-write-assist-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 matthewagi/re-write-assist-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf matthewagi/re-write-assist-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 matthewagi/re-write-assist-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf matthewagi/re-write-assist-gguf:Q4_K_M

Use Docker

docker model run hf.co/matthewagi/re-write-assist-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use matthewagi/re-write-assist-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "matthewagi/re-write-assist-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": "matthewagi/re-write-assist-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/matthewagi/re-write-assist-gguf:Q4_K_M

Ollama
How to use matthewagi/re-write-assist-gguf with Ollama:
```
ollama run hf.co/matthewagi/re-write-assist-gguf:Q4_K_M
```

Unsloth Studio

How to use matthewagi/re-write-assist-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 matthewagi/re-write-assist-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 matthewagi/re-write-assist-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for matthewagi/re-write-assist-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use matthewagi/re-write-assist-gguf with Docker Model Runner:
```
docker model run hf.co/matthewagi/re-write-assist-gguf:Q4_K_M
```

Lemonade

How to use matthewagi/re-write-assist-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull matthewagi/re-write-assist-gguf:Q4_K_M

Run and chat with the model

lemonade run user.re-write-assist-gguf-Q4_K_M

List all available models

lemonade list

Re-Write Assist Q4_K_M

Re-Write Assist is a compact local model for rewriting essay paragraphs in a direct, idea-driven, Paul Graham-inspired manner. It is designed to help students improve their essay skills by comparing an original paragraph with a clearer revision and studying the changes between them.

The model was trained from revision examples produced by a 12-billion-parameter teacher model, supplemented with style-focused paragraph examples. The resulting model has 1.7 billion parameters and is distributed here as a Q4_K_M GGUF for fast local use through llama.cpp.

Intended use

Re-Write Assist is intended for:

revising an existing essay one paragraph at a time;
comparing original and rewritten prose;
studying sentence structure, pacing, clarity, and argument presentation;
running a private writing assistant locally without sending essay text to an external API.

It works best when the source already contains the claims and evidence the writer wants to preserve. Users should review every revision rather than treating the output as authoritative.

Model file

File	Format	Size	SHA-256
`re-write-assist-Q4_K_M.gguf`	GGUF Q4_K_M	1005 MiB	`dcd74248ffa9c375fd53e3625e2c53a8c1e086c193e69e57c3b14e9cad566ebe`

Download with the Hugging Face CLI:

hf download matthewagi/re-write-assist-gguf re-write-assist-Q4_K_M.gguf --local-dir models

Running with llama.cpp

llama-server \
  --model re-write-assist-Q4_K_M.gguf \
  --host 127.0.0.1 \
  --port 8768 \
  --ctx-size 8192 \
  --parallel 2 \
  --gpu-layers all \
  --flash-attn on

The accompanying Re-Write Assist application manages paragraph splitting, prior-paragraph context, prompt construction, local serving, and Original/Changes/Rewritten views automatically.

Quantization

The Q4_K_M file was produced from the merged BF16 model with a task-shaped importance matrix built from essay-rewriting examples. It uses approximately 4.93 bits per weight and is intended to balance local speed, memory use, and rewrite quality.

Limitations

The model can occasionally repeat language or omit, soften, or alter a claim.
Quantization can change an output relative to the higher-precision model.
The PG-inspired direction is a broad writing style target, not a guarantee that every output resembles Paul Graham.
The model should support revision and learning, not replace the student’s own judgment or authorship.

Always compare the rewrite with the source before using it.

Downloads last month: -

GGUF

Model size

2B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for matthewagi/re-write-assist-gguf

Base model

HuggingFaceTB/SmolLM2-1.7B

Quantized

HuggingFaceTB/SmolLM2-1.7B-Instruct

Quantized

(113)

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