Instructions to use OuteAI/Lite-Oute-1-65M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OuteAI/Lite-Oute-1-65M-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="OuteAI/Lite-Oute-1-65M-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OuteAI/Lite-Oute-1-65M-Instruct")
model = AutoModelForCausalLM.from_pretrained("OuteAI/Lite-Oute-1-65M-Instruct")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OuteAI/Lite-Oute-1-65M-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OuteAI/Lite-Oute-1-65M-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OuteAI/Lite-Oute-1-65M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OuteAI/Lite-Oute-1-65M-Instruct

SGLang

How to use OuteAI/Lite-Oute-1-65M-Instruct 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 "OuteAI/Lite-Oute-1-65M-Instruct" \
    --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": "OuteAI/Lite-Oute-1-65M-Instruct",
		"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 "OuteAI/Lite-Oute-1-65M-Instruct" \
        --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": "OuteAI/Lite-Oute-1-65M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OuteAI/Lite-Oute-1-65M-Instruct with Docker Model Runner:
```
docker model run hf.co/OuteAI/Lite-Oute-1-65M-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Lite-Oute-1-65M-Instruct

Lite-Oute-1-65M-Instruct is an experimental ultra-compact model in the Lite series, based on the LLaMA architecture and comprising approximately 65 million parameters.
The primary goal of this model was to explore the lower limits of model size while still maintaining basic language understanding capabilities.
Due to its extremely small size, this model demonstrates basic text generation abilities but may struggle with instructions or maintaining topic coherence.
Users should be aware of its significant limitations compared to larger models and expect inconsistent or potentially inaccurate responses.

Available versions:

Lite-Oute-1-65M-Instruct
Lite-Oute-1-65M-Instruct-GGUF
Lite-Oute-1-65M
Lite-Oute-1-65M-GGUF

Chat format

This model uses ChatML template. Ensure you use the correct template:

<|im_start|>system
[System message]<|im_end|>
<|im_start|>user
[Your question or message]<|im_end|>
<|im_start|>assistant
[The model's response]<|im_end|>

Benchmark	5-shot	0-shot
ARC Challenge	22.61	23.63
ARC Easy	37.16	40.49
CommonsenseQA	19.41	20.64
HellaSWAG	28.74	28.41
MMLU	25.20	23.45
OpenBookQA	27.40	28.60
PIQA	60.88	60.77
Winogrande	50.59	50.04

Usage with HuggingFace transformers

The model can be used with HuggingFace's transformers library:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModelForCausalLM.from_pretrained("OuteAI/Lite-Oute-1-65M-Instruct").to(device)
tokenizer = AutoTokenizer.from_pretrained("OuteAI/Lite-Oute-1-65M-Instruct")
def generate_response(message: str, temperature: float = 0.4, repetition_penalty: float = 1.12) -> str:
    # Apply the chat template and convert to PyTorch tensors
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": message}
    ]
    input_ids = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_tensors="pt"
    ).to(device)
    # Generate the response
    output = model.generate(
        input_ids,
        max_length=512,
        temperature=temperature,
        repetition_penalty=repetition_penalty,
        do_sample=True
    ) 
    # Decode the generated output
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    return generated_text
message = "I'd like to learn about language models. Can you break down the concept for me?"
response = generate_response(message)
print(response)

Risk Disclaimer

By using this model, you acknowledge that you understand and assume the risks associated with its use. You are solely responsible for ensuring compliance with all applicable laws and regulations. We disclaim any liability for problems arising from the use of this open-source model, including but not limited to direct, indirect, incidental, consequential, or punitive damages. We make no warranties, express or implied, regarding the model's performance, accuracy, or fitness for a particular purpose. Your use of this model is at your own risk, and you agree to hold harmless and indemnify us, our affiliates, and our contributors from any claims, damages, or expenses arising from your use of the model.