EXAONE-Deep-2.4B GGUF Models

Choosing the Right Model Format

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.

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.

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

• 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.

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

• 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

Included Files & Details

EXAONE-Deep-2.4B-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-2.4B-f16.gguf

• Model weights stored in F16.
• Use if your device supports FP16, especially if BF16 is not available.

EXAONE-Deep-2.4B-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-2.4B-f16-q8_0.gguf

• Output & embeddings remain in F16.
• All other layers quantized to Q8_0.

EXAONE-Deep-2.4B-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-2.4B-q4_k_s.gguf

• Smallest Q4_K variant, using less memory at the cost of accuracy.
• Best for very low-memory setups.

EXAONE-Deep-2.4B-q6_k.gguf

• Output & embeddings quantized to Q8_0.
• All other layers quantized to Q6_K .

EXAONE-Deep-2.4B-q8_0.gguf

• Fully Q8 quantized model for better accuracy.
• Requires more memory but offers higher precision.

EXAONE-Deep-2.4B-iq3_xs.gguf

• IQ3_XS quantization, optimized for extreme memory efficiency.
• Best for ultra-low-memory devices.

EXAONE-Deep-2.4B-iq3_m.gguf

• IQ3_M quantization, offering a medium block size for better accuracy.
• Suitable for low-memory devices.

EXAONE-Deep-2.4B-q4_0.gguf

• Pure Q4_0 quantization, optimized for ARM devices.
• Best for low-memory environments.
• Prefer IQ4_NL for better accuracy.

🚀 If you find these models useful

What I'm Testing

The other Available AI Assistants

Final Word

EXAONE-Deep-2.4B

Introduction

• Number of Parameters (without embeddings): 2.14B
• Number of Layers: 30
• Number of Attention Heads: GQA with 32 Q-heads and 8 KV-heads
• Vocab Size: 102,400
• Context Length: 32,768 tokens
• Tie Word Embeddings: True (unlike 7.8B and 32B models)

Note

The EXAONE Deep models are trained with an optimized configuration, so we recommend following the Usage Guideline section to achieve optimal performance.

Evaluation

Deployment

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

Quantization

Usage Guideline

1. Ensure the model starts with <thought> 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> ... </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

• 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.

License

Citation

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

Contact