SmolLM-1.7B-Instruct-GGUF

SandLogic Technology - Quantized SmolLM-1.7B-Instruct Models

Model Description

We have quantized the SmolLM-1.7B-Instruct model into three variants:

1. Q5_KM
2. Q4_KM
3. IQ4_XS

These quantized models offer improved efficiency while maintaining performance.

Discover our full range of quantized language models by visiting our SandLogic Lexicon GitHub. To learn more about our company and services, check out our website at SandLogic.

Original Model Information

• Name: SmolLM-1.7B-Instruct
• Model Type: Small language model
• Parameters: 1.7 billion
• Training Data: SmolLM-Corpus (curated high-quality educational and synthetic data)

Model Capabilities

SmolLM-1.7B-Instruct is designed for various natural language processing tasks, with capabilities including:

• General knowledge question answering
• Creative writing
• Basic Python programming

Finetuning Details

The model was finetuned on a mixture of datasets, including:

• 2k simple everyday conversations generated by llama3.1-70B
• Magpie-Pro-300K-Filtered
• StarCoder2-Self-OSS-Instruct
• A small subset of OpenHermes-2.5

Limitations

• English language only
• May struggle with arithmetic, editing tasks, and complex reasoning
• Generated content may not always be factually accurate or logically consistent
• Potential biases from training data

Intended Use

1. Educational Assistance: Helping students with general knowledge questions and basic programming concepts.
2. Creative Writing Aid: Assisting in generating ideas or outlines for creative writing projects.
3. Conversational AI: Powering chatbots for simple, everyday conversations.
4. Code Completion: Providing suggestions for basic Python programming tasks.
5. General Knowledge Queries: Answering straightforward questions on various topics.

Model Variants

We offer three quantized versions of the SmolLM-1.7B-Instruct model:

1. Q5_KM: 5-bit quantization using the KM method
2. Q4_KM: 4-bit quantization using the KM method
3. IQ4_XS: 4-bit quantization using the IQ4_XS method

These quantized models aim to reduce model size and improve inference speed while maintaining performance as close to the original model as possible.

Usage

pip install llama-cpp-python 

Please refer to the llama-cpp-python documentation to install with GPU support.

Basic Text Completion

Here's an example demonstrating how to use the high-level API for basic text completion:

from llama_cpp import Llama

llm = Llama(
    model_path="./models/SmolLM-1.7B-Instruct.Q5_K_M.gguf",
    verbose=False,
    # n_gpu_layers=-1, # Uncomment to use GPU acceleration
    # n_ctx=2048, # Uncomment to increase the context window
)

output = llm.create_chat_completion(
    messages = [
        {"role": "system", "content": "You're an AI assistant who help the user to answer his questions"},
        {
            "role": "user",
            "content": "What is the capital of France."
        }
    ]
)

print(output["choices"][0]['message']['content'])

Download

You can download Llama models in gguf format directly from Hugging Face using the from_pretrained method. This feature requires the huggingface-hub package.

To install it, run: pip install huggingface-hub

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="SandLogicTechnologies/SmolLM-1.7B-Instruct-GGUF",
    filename="*SmolLM-1.7B-Instruct.Q5_K_M.gguf",
    verbose=False
)

By default, from_pretrained will download the model to the Hugging Face cache directory. You can manage installed model files using the huggingface-cli tool.

Acknowledgements

We thank the original developers of SmolLM for their contributions to the field of small language models. Special thanks to Georgi Gerganov and the entire llama.cpp development team for their outstanding contributions.

Contact

For any inquiries or support, please contact us at [email protected] or visit our support page.