Instructions to use QuantFactory/arcee-lite-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/arcee-lite-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/arcee-lite-GGUF",
	filename="arcee-lite.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/arcee-lite-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 QuantFactory/arcee-lite-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/arcee-lite-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 QuantFactory/arcee-lite-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/arcee-lite-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 QuantFactory/arcee-lite-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/arcee-lite-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 QuantFactory/arcee-lite-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/arcee-lite-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/arcee-lite-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/arcee-lite-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/arcee-lite-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/arcee-lite-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 QuantFactory/arcee-lite-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 QuantFactory/arcee-lite-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/arcee-lite-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use QuantFactory/arcee-lite-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/arcee-lite-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/arcee-lite-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/arcee-lite-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.arcee-lite-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/arcee-lite-GGUF

This is quantized version of arcee-ai/arcee-lite created using llama.cpp

Original Model Card

Arcee-Lite is a compact yet powerful 1.5B parameter language model developed as part of the DistillKit open-source project. Despite its small size, Arcee-Lite demonstrates impressive performance, particularly in the MMLU (Massive Multitask Language Understanding) benchmark.

GGUFS available here

Key Features

Model Size: 1.5 billion parameters
MMLU Score: 55.93
Distillation Source: Phi-3-Medium
Enhanced Performance: Merged with high-performing distillations

About DistillKit

DistillKit is our new open-source project focused on creating efficient, smaller models that maintain high performance. Arcee-Lite is one of the first models to emerge from this initiative.

Performance

Arcee-Lite showcases remarkable capabilities for its size:

Achieves a 55.93 score on the MMLU benchmark
Demonstrates exceptional performance across various tasks

Use Cases

Arcee-Lite is suitable for a wide range of applications where a balance between model size and performance is crucial:

Embedded systems
Mobile applications
Edge computing
Resource-constrained environments

Please note that our internal evaluations were consistantly higher than their counterparts on the OpenLLM Leaderboard - and should only be compared against the relative performance between the models, not weighed against the leaderboard.

Downloads last month: 3

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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