Hyperclovax SEED Text Instruct 0.5B GGUF

🚀 HyperCLOVAX-SEED-Text-Instruct-0.5B GGUF Models

HyperCLOVAX-SEED-Text-Instruct-0.5B GGUF models are text-generation models. They are designed to understand Korean language and culture well, with improved mathematical performance and enhanced Korean language capabilities compared to similar models. These lightweight models are suitable for resource-constrained environments like edge devices.

📚 Documentation

Model Generation Details

This model was generated using llama.cpp at commit 5e7d95e2.

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.

⚠️ Important Note

Use BF16 if your hardware has native BF16 support (e.g., newer GPUs, TPUs), you want higher precision while saving memory, or you plan to requantize the model into another format. Avoid it if your hardware does not support BF16 (it may fall back to FP32 and run slower) or 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.

⚠️ Important Note

Use F16 if your hardware supports FP16 but not BF16, you need a balance between speed, memory usage, and accuracy, or you are running on a GPU or another device optimized for FP16 computations. Avoid it if your device lacks native FP16 support (it may run slower than expected) or 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.

⚠️ Important Note

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, or you want to reduce memory footprint while keeping reasonable accuracy. Avoid them if you need maximum accuracy (full-precision models are better for this) or 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

Included Files & Details

If you find these models useful

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Basic Information

Training and Data

The training dataset for HyperCLOVAX-SEED-Text-Instruct-0.5B consists of diverse sources, including the high‑quality data accumulated during the development of HyperCLOVAX-SEED-Text-Instruct-0.5B. Training was conducted in three main stages:

1. Pretraining: Knowledge acquisition using high‑quality data and a high‑performance pretrained model.
2. Rejection Sampling Fine‑Tuning (RFT): Enhancement of multi‑domain knowledge and complex reasoning capabilities.
3. Supervised Fine‑Tuning (SFT): Improvement of instruction‑following proficiency.

Training Cost

HyperCLOVAX-SEED-Text-Instruct-0.5B leveraged HyperCLOVA X’s lightweight training process and high‑quality data to achieve significantly lower training costs compared to industry‑leading competitors of similar scale. Excluding the SFT stage, a single pretraining run incurred:

This represents approximately a 39× reduction in pretraining cost relative to QWEN2.5‑0.5B-instruct.

Benchmarks

💻 Usage Examples

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B").to(device="cuda")
tokenizer = AutoTokenizer.from_pretrained("naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B")

chat = [
  {"role": "tool_list", "content": ""},
  {"role": "system", "content": "- AI 언어모델의 이름은 \"CLOVA X\" 이며 네이버에서 만들었다.\n- 오늘은 2025년 04월 24일(목)이다."},
  {"role": "user", "content": "슈뢰딩거 방정식과 양자역학의 관계를 최대한 자세"}

Advanced Usage

No advanced usage example is provided in the original document.

📄 License

The license for these models is the hyperclovax-seed license. You can find more details in the LICENSE file.