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

Libraries

How to use arcee-ai/arcee-lite with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="arcee-ai/arcee-lite")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arcee-ai/arcee-lite")
model = AutoModelForCausalLM.from_pretrained("arcee-ai/arcee-lite")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use arcee-ai/arcee-lite with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arcee-ai/arcee-lite"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcee-ai/arcee-lite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/arcee-ai/arcee-lite

SGLang

How to use arcee-ai/arcee-lite with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "arcee-ai/arcee-lite" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcee-ai/arcee-lite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "arcee-ai/arcee-lite" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcee-ai/arcee-lite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use arcee-ai/arcee-lite with Docker Model Runner:
```
docker model run hf.co/arcee-ai/arcee-lite
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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: 1,077

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for arcee-ai/arcee-lite

Finetunes

5 models

Merges

1 model

Quantizations

7 models