Instructions to use oblivious/Refact-1.6B-fim-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use oblivious/Refact-1.6B-fim-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="oblivious/Refact-1.6B-fim-GGUF",
	filename="refact-1_6b-Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use oblivious/Refact-1.6B-fim-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf oblivious/Refact-1.6B-fim-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf oblivious/Refact-1.6B-fim-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf oblivious/Refact-1.6B-fim-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf oblivious/Refact-1.6B-fim-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 oblivious/Refact-1.6B-fim-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf oblivious/Refact-1.6B-fim-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 oblivious/Refact-1.6B-fim-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf oblivious/Refact-1.6B-fim-GGUF:Q4_K_M

Use Docker

docker model run hf.co/oblivious/Refact-1.6B-fim-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use oblivious/Refact-1.6B-fim-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "oblivious/Refact-1.6B-fim-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oblivious/Refact-1.6B-fim-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/oblivious/Refact-1.6B-fim-GGUF:Q4_K_M

Ollama
How to use oblivious/Refact-1.6B-fim-GGUF with Ollama:
```
ollama run hf.co/oblivious/Refact-1.6B-fim-GGUF:Q4_K_M
```

Unsloth Studio

How to use oblivious/Refact-1.6B-fim-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 oblivious/Refact-1.6B-fim-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 oblivious/Refact-1.6B-fim-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for oblivious/Refact-1.6B-fim-GGUF to start chatting

Docker Model Runner
How to use oblivious/Refact-1.6B-fim-GGUF with Docker Model Runner:
```
docker model run hf.co/oblivious/Refact-1.6B-fim-GGUF:Q4_K_M
```

Lemonade

How to use oblivious/Refact-1.6B-fim-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull oblivious/Refact-1.6B-fim-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Refact-1.6B-fim-GGUF-Q4_K_M

List all available models

lemonade list

Refact-1.6B-fim-GGUF

Model creator: Small Magellanic Cloud AI
Original model: Refact-1.6B

Description

This repository contains quantized GGUF format model files for Refact-1.6B.

Prompt: fill in the middle

<fim_prefix>def print_hello_world():\n    """<fim_suffix>\n    print("Hello world!")<fim_middle>

Prompt: chat (experimental)

<empty_output>SYSTEM You are a programming assistant
<empty_output>USER How do I sort a list in Python?
<empty_output>ASSISTANT

Example `llama.cpp` command

./main -m refact-1_6b-Q4_K_M.gguf -c 4096 -n -1 -p '<fim_prefix>{prefix}<fim_suffix>{suffix}<fim_middle>'

For other parameters and how to use them, please refer to the llama.cpp documentation

Downloads last month: 225

GGUF

Model size

2B params

Architecture

refact

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for oblivious/Refact-1.6B-fim-GGUF

Base model

refactai/Refact-1_6B-fim

Quantized

(7)

this model

oblivious
/

Refact-1.6B-fim-GGUF

Refact-1.6B-fim-GGUF

Description

Prompt: fill in the middle

Prompt: chat (experimental)

Example `llama.cpp` command

Model tree for oblivious/Refact-1.6B-fim-GGUF

Datasets used to train oblivious/Refact-1.6B-fim-GGUF

Space using oblivious/Refact-1.6B-fim-GGUF 1

Refact-1.6B-fim-GGUF

Description

Prompt: fill in the middle

Prompt: chat (experimental)

Example llama.cpp command

Model tree for oblivious/Refact-1.6B-fim-GGUF

Datasets used to train oblivious/Refact-1.6B-fim-GGUF

Space using oblivious/Refact-1.6B-fim-GGUF 1

Example `llama.cpp` command