EXAONE Deep 7.8B GGUF

🚀 EXAONE-Deep-7.8B GGUF Models

EXAONE-Deep-7.8B GGUF Models offer advanced text generation capabilities with ultra-low-bit quantization techniques. These models are designed to balance memory efficiency and accuracy, making them suitable for a wide range of applications, from high-performance inference on powerful hardware to low-memory deployments on edge devices.

✨ Features

Ultra-Low-Bit Quantization with IQ-DynamicGate (1-2 bit)

Our latest quantization method introduces precision-adaptive quantization for ultra-low-bit models (1-2 bit), with benchmark-proven improvements on Llama-3-8B. This approach uses layer-specific strategies to preserve accuracy while maintaining extreme memory efficiency.

Benchmark Context

All tests conducted on Llama-3-8B-Instruct using:

• Standard perplexity evaluation pipeline
• 2048-token context window
• Same prompt set across all quantizations

Method

• Dynamic Precision Allocation:
  • First/Last 25% of layers → IQ4_XS (selected layers)
  • Middle 50% → IQ2_XXS/IQ3_S (increase efficiency)
• Critical Component Protection:
  • Embeddings/output layers use Q5_K
  • Reduces error propagation by 38% vs standard 1-2bit

Quantization Performance Comparison (Llama-3-8B)

Key:

• PPL = Perplexity (lower is better)
• Δ PPL = Percentage change from standard to DynamicGate
• Speed = Inference time (CPU avx2, 2048 token context)
• Size differences reflect mixed quantization overhead

Key Improvements:

• 🔥 IQ1_M shows massive 43.9% perplexity reduction (27.46 → 15.41)
• 🚀 IQ2_S cuts perplexity by 36.9% while adding only 0.2GB
• ⚡ IQ1_S maintains 39.7% better accuracy despite 1-bit quantization

Tradeoffs:

• All variants have modest size increases (0.1-0.3GB)
• Inference speeds remain comparable (<5% difference)

When to Use These Models

📌 Fitting models into GPU VRAM ✔ Memory-constrained deployments ✔ Cpu and Edge Devices where 1-2bit errors can be tolerated ✔ Research into ultra-low-bit quantization

Choosing the Right Model Format

Selecting the correct model format depends on your hardware capabilities and memory constraints.

BF16 (Brain Float 16) – Use if BF16 acceleration is available

• A 16-bit floating-point format designed for faster computation while retaining good precision.
• Provides similar dynamic range as FP32 but with lower memory usage.
• Recommended if your hardware supports BF16 acceleration (check your device's specs).
• Ideal for high-performance inference with reduced memory footprint compared to FP32.

📌 Use BF16 if: ✔ Your hardware has native BF16 support (e.g., newer GPUs, TPUs). ✔ You want higher precision while saving memory. ✔ You plan to requantize the model into another format.

📌 Avoid BF16 if: ❌ Your hardware does not support BF16 (it may fall back to FP32 and run slower). ❌ You need compatibility with older devices that lack BF16 optimization.

F16 (Float 16) – More widely supported than BF16

• A 16-bit floating-point high precision but with less of range of values than BF16.
• Works on most devices with FP16 acceleration support (including many GPUs and some CPUs).
• Slightly lower numerical precision than BF16 but generally sufficient for inference.

📌 Use F16 if: ✔ Your hardware supports FP16 but not BF16. ✔ You need a balance between speed, memory usage, and accuracy. ✔ You are running on a GPU or another device optimized for FP16 computations.

📌 Avoid F16 if: ❌ Your device lacks native FP16 support (it may run slower than expected). ❌ You have memory limitations.

Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference

Quantization reduces model size and memory usage while maintaining as much accuracy as possible.

• Lower-bit models (Q4_K) → Best for minimal memory usage, may have lower precision.
• Higher-bit models (Q6_K, Q8_0) → Better accuracy, requires more memory.

📌 Use Quantized Models if: ✔ You are running inference on a CPU and need an optimized model. ✔ Your device has low VRAM and cannot load full-precision models. ✔ You want to reduce memory footprint while keeping reasonable accuracy.

📌 Avoid Quantized Models if: ❌ You need maximum accuracy (full-precision models are better for this). ❌ Your hardware has enough VRAM for higher-precision formats (BF16/F16).

Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)

These models are optimized for extreme memory efficiency, making them ideal for low-power devices or large-scale deployments where memory is a critical constraint.

• IQ3_XS: Ultra-low-bit quantization (3-bit) with extreme memory efficiency.
  • Use case: Best for ultra-low-memory devices where even Q4_K is too large.
  • Trade-off: Lower accuracy compared to higher-bit quantizations.
• IQ3_S: Small block size for maximum memory efficiency.
  • Use case: Best for low-memory devices where IQ3_XS is too aggressive.
• IQ3_M: Medium block size for better accuracy than IQ3_S.
  • Use case: Suitable for low-memory devices where IQ3_S is too limiting.
• Q4_K: 4-bit quantization with block-wise optimization for better accuracy.
  • Use case: Best for low-memory devices where Q6_K is too large.
• Q4_0: Pure 4-bit quantization, optimized for ARM devices.
  • Use case: Best for ARM-based devices or low-memory environments.

Summary Table: Model Format Selection

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread

model_name = "LGAI-EXAONE/EXAONE-Deep-7.8B"
streaming = True    # choose the streaming option

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Choose your prompt:
#   Math example (AIME 2024)
prompt = r"""Let $x,y$ and $z$ be positive real numbers that satisfy the following system of equations:
\[\log_2\left({x \over yz}\right) = {1 \over 2}\]\[\log_2\left({y \over xz}\right) = {1 \over 3}\]\[\log_2\left({z \over xy}\right) = {1 \over 4}\]
Then the value of $\left|\log_2(x^4y^3z^2)\right|$ is $\tfrac{m}{n}$ where $m$ and $n$ are relatively prime positive integers. Find $m+n$.

Please reason step by step, and put your final answer within \boxed{}."""
#   Korean MCQA example (CSAT Math 2025)
prompt = r"""Question : $a_1 = 2$인 수열 $\{a_n\}$과 $b_1 = 2$인 등차수열 $\{b_n\}$이 모든 자연수 $n$에 대하여\[\sum_{k=1}^{n} \frac{a_k}{b_{k+1}} = \frac{1}{2} n^2\]을 만족시킬 때, $\sum_{k=1}^{5} a_k$의 값을 구하여라.

Options :
A) 120
B) 125
C) 130
D) 135
E) 140

Please reason step by step, and you should write the correct option alphabet (A, B, C, D or E) within \\boxed{}."""

messages = [
    {"role": "user", "content": prompt}
]
input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
)

if streaming:
    streamer = TextIteratorStreamer(tokenizer)
    thread = Thread(target=model.generate, kwargs=dict(
        input_ids=input_ids.to("cuda"),
        eos_token_id=tokenizer.eos_token_id,
        max_new_tokens=32768,
        do_sample=True,
        temperature=0.6,
        top_p=0.95,
        streamer=streamer
    ))
    thread.start()

    for text in streamer:
        print(text, end="", flush=True)
else:
    output = model.generate(
        input_ids.to("cuda"),
        eos_token_id=tokenizer.eos_token_id,
        max_new_tokens=32768,
        do_sample=True,
        temperature=0.6,
        top_p=0.95,
    )
    print(tokenizer.decode(output[0]))

Advanced Usage

There is no advanced usage example in the original document.

📚 Documentation

Included Files & Details

• EXAONE-Deep-7.8B-bf16.gguf: Model weights preserved in BF16. Use this if you want to requantize the model into a different format. Best if your device supports BF16 acceleration.
• EXAONE-Deep-7.8B-f16.gguf: Model weights stored in F16. Use if your device supports FP16, especially if BF16 is not available.
• EXAONE-Deep-7.8B-bf16-q8_0.gguf: Output & embeddings remain in BF16. All other layers quantized to Q8_0. Use if your device supports BF16 and you want a quantized version.
• EXAONE-Deep-7.8B-f16-q8_0.gguf: Output & embeddings remain in F16. All other layers quantized to Q8_0.
• EXAONE-Deep-7.8B-q4_k.gguf: Output & embeddings quantized to Q8_0. All other layers quantized to Q4_K. Good for CPU inference with limited memory.
• EXAONE-Deep-7.8B-q4_k_s.gguf: Smallest Q4_K variant, using less memory at the cost of accuracy. Best for very low-memory setups.
• EXAONE-Deep-7.8B-q6_k.gguf: Output & embeddings quantized to Q8_0. All other layers quantized to Q6_K.
• EXAONE-Deep-7.8B-q8_0.gguf: Fully Q8 quantized model for better accuracy. Requires more memory but offers higher precision.
• EXAONE-Deep-7.8B-iq3_xs.gguf: IQ3_XS quantization, optimized for extreme memory efficiency. Best for ultra-low-memory devices.
• EXAONE-Deep-7.8B-iq3_m.gguf: IQ3_M quantization, offering a medium block size for better accuracy. Suitable for low-memory devices.
• EXAONE-Deep-7.8B-q4_0.gguf: Pure Q4_0 quantization, optimized for ARM devices. Best for low-memory environments. Prefer IQ4_NL for better accuracy.

Testing the Models

If you find these models useful: ❤ Please click "Like" if you find this useful! Help me test my AI-Powered Network Monitor Assistant with quantum-ready security checks: 👉 Free Network Monitor

💬 How to test:

1. Click the chat icon (bottom right on any page)
2. Choose an AI assistant type:
   • TurboLLM (GPT-4-mini)
   • FreeLLM (Open-source)
   • TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

• Function calling against live network services
• How small can a model go while still handling:
  • Automated Nmap scans
  • Quantum-readiness checks
  • Metasploit integration

🟡 TestLLM – Current experimental model (llama.cpp on 6 CPU threads):

• ✅ Zero-configuration setup
• ⏳ 30s load time (slow inference but no API costs)
• 🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4-mini for:

• Real-time network diagnostics
• Automated penetration testing (Nmap/Metasploit)
• 🔑 Get more tokens by downloading our Free Network Monitor Agent

🔵 HugLLM – Open-source models (≈8B params):

• 2x more tokens than TurboLLM
• AI-powered log analysis
• 🌐 Runs on Hugging Face Inference API

Example AI Commands to Test

1. "Give me info on my websites SSL certificate"
2. "Check if my server is using quantum safe encyption for communication"
3. "Run a quick Nmap vulnerability test"

🔧 Technical Details

Introduction

We introduce EXAONE Deep, which exhibits superior capabilities in various reasoning tasks including math and coding benchmarks, ranging from 2.4B to 32B parameters developed and released by LG AI Research. Evaluation results show that 1) EXAONE Deep 2.4B outperforms other models of comparable size, 2) EXAONE Deep 7.8B outperforms not only open-weight models of comparable scale but also a proprietary reasoning model OpenAI o1-mini, and 3) EXAONE Deep 32B demonstrates competitive performance against leading open-weight models.

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

Model 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: 102,400
• Context Length: 32,768 tokens

Evaluation

The following table shows the evaluation results of reasoning tasks such as math and coding. The full evaluation results can be found in the documentation.

Deployment

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

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

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

Quantization

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

Usage Guideline

To achieve the expected performance, we recommend using the following configurations:

1. Ensure the model starts with <thought>\n for reasoning steps. The model's output quality may be degraded when you omit it. You can easily apply this feature by using tokenizer.apply_chat_template() with add_generation_prompt=True. Please check the example code on Quickstart section.
2. The reasoning steps of EXAONE Deep models enclosed by <thought>\n...\n</thought> usually have lots of tokens, so previous reasoning steps may be necessary to be removed in multi-turn situation. The provided tokenizer handles this automatically.
3. Avoid using system prompt, and build the instruction on the user prompt.
4. Additional instructions help the models reason more deeply, so that the models generate better output.
   • For math problems, the instructions "Please reason step by step, and put your final answer within \boxed{}." are helpful.
   • For more information on our evaluation setting including prompts, please refer to our Documentation.
5. In our evaluation, we use temperature=0.6 and top_p=0.95 for generation.
6. When evaluating the models, it is recommended to test multiple times to assess the expected performance accurately.

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 models. 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 models.

📄 License

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

Citation

@article{exaone-deep,
  title={EXAONE Deep: Reasoning Enhanced Language Models},
  author={{LG AI Research}},
  journal={arXiv preprint arXiv:2503.12524},
  year={2025}
}

Contact

LG AI Research Technical Support: contact_us@lgresearch.ai