Instructions to use spotapovadm/sweep-next-edit-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spotapovadm/sweep-next-edit-1.5B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="spotapovadm/sweep-next-edit-1.5B",
	filename="sweep-next-edit-1.5b.q8_0.v2.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use spotapovadm/sweep-next-edit-1.5B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf spotapovadm/sweep-next-edit-1.5B:Q8_0
# Run inference directly in the terminal:
llama-cli -hf spotapovadm/sweep-next-edit-1.5B:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf spotapovadm/sweep-next-edit-1.5B:Q8_0
# Run inference directly in the terminal:
llama-cli -hf spotapovadm/sweep-next-edit-1.5B:Q8_0

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 spotapovadm/sweep-next-edit-1.5B:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf spotapovadm/sweep-next-edit-1.5B:Q8_0

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 spotapovadm/sweep-next-edit-1.5B:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf spotapovadm/sweep-next-edit-1.5B:Q8_0

Use Docker

docker model run hf.co/spotapovadm/sweep-next-edit-1.5B:Q8_0

LM Studio
Jan
Ollama
How to use spotapovadm/sweep-next-edit-1.5B with Ollama:
```
ollama run hf.co/spotapovadm/sweep-next-edit-1.5B:Q8_0
```

Unsloth Studio new

How to use spotapovadm/sweep-next-edit-1.5B 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 spotapovadm/sweep-next-edit-1.5B 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 spotapovadm/sweep-next-edit-1.5B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for spotapovadm/sweep-next-edit-1.5B to start chatting

Docker Model Runner
How to use spotapovadm/sweep-next-edit-1.5B with Docker Model Runner:
```
docker model run hf.co/spotapovadm/sweep-next-edit-1.5B:Q8_0
```

Lemonade

How to use spotapovadm/sweep-next-edit-1.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull spotapovadm/sweep-next-edit-1.5B:Q8_0

Run and chat with the model

lemonade run user.sweep-next-edit-1.5B-Q8_0

List all available models

lemonade list

Sweep Next-Edit 1.5B (GGUF)

A 1.5B parameter model for next-edit autocomplete, quantized to Q8_0 GGUF format.

Model Description

Sweep Next-Edit predicts your next code edit before you make it. It runs locally on your laptop in under 500ms (with speculative decoding) and outperforms models over 4x its size on next-edit benchmarks. More details here.

Usage

Download run_model.py and the model file, then:

uv pip install llama-cpp-python huggingface_hub
python run_model.py

Model Details

Format: GGUF (Q8_0 quantization)
Parameters: 1.5B
Context Length: 8192 tokens
Base Model: Qwen2.5-Coder

Example

The model uses a specific prompt format with file context, recent diffs, and current state to predict the next edit. See run_model.py for a complete example.

License

Apache 2.0

Downloads last month: 12

GGUF

Model size

1B params

Architecture

qwen2

Hardware compatibility

8-bit

Inference Providers NEW

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