EXAONE-3.5-7.8B-Instruct-GGUF

Introduction

We introduce EXAONE 3.5, a collection of instruction-tuned bilingual (English and Korean) generative models ranging from 2.4B to 32B parameters, developed and released by LG AI Research. EXAONE 3.5 language models include: 1) 2.4B model optimized for deployment on small or resource-constrained devices, 2) 7.8B model matching the size of its predecessor but offering improved performance, and 3) 32B model delivering powerful performance. All models support long-context processing of up to 32K tokens. Each model demonstrates state-of-the-art performance in real-world use cases and long-context understanding, while remaining competitive in general domains compared to recently released models of similar sizes.

For more details, please refer to our technical report, blog and GitHub.

This repository contains the various precisions of the instruction-tuned 7.8B language model in GGUF format, which contains the following features:

• Number of Parameters (without embeddings): 6.98B
• Number of Layers: 32
• Number of Attention Heads: GQA with 32 Q-heads and 8 KV-heads
• Vocab Size: 102400
• Context Length: 32,768 tokens
• Quantization: Q8_0, Q6_0, Q5_K_M, Q4_K_M, IQ4_XS in GGUF format (also includes BF16 weights)

Please refer to the HugggingFace GGUF documentation for more details about the precisions in GGUF format.

Quickstart

Here are the steps to run conversational inference with the model:

1. Install llama.cpp. Please refer to the llama.cpp repository for more details.

2. Download EXAONE 3.5 model in GGUF format.

huggingface-cli download LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-GGUF \
    --include "EXAONE-3.5-7.8B-Instruct-BF16*.gguf" \
    --local-dir .

3. Run the model with llama.cpp in conversational mode.

llama-cli -cnv -m ./EXAONE-3.5-7.8B-Instruct-BF16.gguf \
    -p "You are EXAONE model from LG AI Research, a helpful assistant."

Note

The EXAONE 3.5 instruction-tuned language models were trained to utilize the system prompt, so we highly recommend using the system prompts provided in the code snippet above.

Deployment

EXAONE 3.5 models can be inferred in the various frameworks, such as:

• TensorRT-LLM
• vLLM
• SGLang
• llama.cpp
• Ollama

Please refer to our EXAONE 3.5 GitHub for more details about the inference frameworks.

Quantization

We provide the pre-quantized EXAONE 3.5 models with AWQ and several quantization types in GGUF format. Please refer to our EXAONE 3.5 collection to find corresponding quantized models.

Limitation

The EXAONE language model has certain limitations and may occasionally generate inappropriate responses. The language model generates responses based on the output probability of tokens, and it is determined during learning from training data. While we have made every effort to exclude personal, harmful, and biased information from the training data, some problematic content may still be included, potentially leading to undesirable responses. Please note that the text generated by EXAONE language model does not reflects the views of LG AI Research.

• Inappropriate answers may be generated, which contain personal, harmful or other inappropriate information.
• Biased responses may be generated, which are associated with age, gender, race, and so on.
• The generated responses rely heavily on statistics from the training data, which can result in the generation of semantically or syntactically incorrect sentences.
• Since the model does not reflect the latest information, the responses may be false or contradictory.

LG AI Research strives to reduce potential risks that may arise from EXAONE language model. Users are not allowed to engage in any malicious activities (e.g., keying in illegal information) that may induce the creation of inappropriate outputs violating LG AI’s ethical principles when using EXAONE language model.

License

The model is licensed under EXAONE AI Model License Agreement 1.1 - NC

Citation

@article{exaone-3.5,
  title={EXAONE 3.5: Series of Large Language Models for Real-world Use Cases},
  author={LG AI Research},
  journal={arXiv preprint arXiv:https://arxiv.org/abs/2412.04862},
  year={2024}
}

Contact

LG AI Research Technical Support: contact_us@lgresearch.ai