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:# Run inference directly in the terminal:
llama cli -hf QuantFactory/arcee-lite-GGUF: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:# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/arcee-lite-GGUF: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:# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/arcee-lite-GGUF:Use Docker
docker model run hf.co/QuantFactory/arcee-lite-GGUF:
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.
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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/arcee-lite-GGUF:# Run inference directly in the terminal: llama cli -hf QuantFactory/arcee-lite-GGUF: