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

llama.cpp

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

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 new

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

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

aashish1904 commited on Aug 1, 2024

Commit

4d83464

verified ·

1 Parent(s): efcc1de

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	@@ -0,0 +1,58 @@

+---
+license: apache-2.0
+---
+![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
+# QuantFactory/arcee-lite-GGUF
+This is quantized version of [arcee-ai/arcee-lite](https://huggingface.co/arcee-ai/arcee-lite) created using llama.cpp
+# Original Model Card
+<div align="center">
+  <img src="https://i.ibb.co/g9Z2CGQ/arcee-lite.webp" alt="Arcee-Lite" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;">
+</div>
+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](https://huggingface.co/arcee-ai/arcee-lite-GGUF)
+## 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
+<div align="center">
+  <img src="https://i.ibb.co/hDC7WBt/Screenshot-2024-08-01-at-8-59-33-AM.png" alt="Arcee-Lite" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;">
+</div>
+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.
+---