library_name: transformers
tags: []
Qwen3-8B-abliterated GGUF Models
Model Generation Details
This model was generated using llama.cpp at commit 19e899c.
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)
| Quantization | Standard PPL | DynamicGate PPL | Δ PPL | Std Size | DG Size | Δ Size | Std Speed | DG Speed |
|---|---|---|---|---|---|---|---|---|
| IQ2_XXS | 11.30 | 9.84 | -12.9% | 2.5G | 2.6G | +0.1G | 234s | 246s |
| IQ2_XS | 11.72 | 11.63 | -0.8% | 2.7G | 2.8G | +0.1G | 242s | 246s |
| IQ2_S | 14.31 | 9.02 | -36.9% | 2.7G | 2.9G | +0.2G | 238s | 244s |
| IQ1_M | 27.46 | 15.41 | -43.9% | 2.2G | 2.5G | +0.3G | 206s | 212s |
| IQ1_S | 53.07 | 32.00 | -39.7% | 2.1G | 2.4G | +0.3G | 184s | 209s |
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
| Model Format | Precision | Memory Usage | Device Requirements | Best Use Case |
|---|---|---|---|---|
| BF16 | Highest | High | BF16-supported GPU/CPUs | High-speed inference with reduced memory |
| F16 | High | High | FP16-supported devices | GPU inference when BF16 isn't available |
| Q4_K | Medium Low | Low | CPU or Low-VRAM devices | Best for memory-constrained environments |
| Q6_K | Medium | Moderate | CPU with more memory | Better accuracy while still being quantized |
| Q8_0 | High | Moderate | CPU or GPU with enough VRAM | Best accuracy among quantized models |
| IQ3_XS | Very Low | Very Low | Ultra-low-memory devices | Extreme memory efficiency and low accuracy |
| Q4_0 | Low | Low | ARM or low-memory devices | llama.cpp can optimize for ARM devices |
Included Files & Details
Qwen3-8B-abliterated-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.
Qwen3-8B-abliterated-f16.gguf
- Model weights stored in F16.
- Use if your device supports FP16, especially if BF16 is not available.
Qwen3-8B-abliterated-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.
Qwen3-8B-abliterated-f16-q8_0.gguf
- Output & embeddings remain in F16.
- All other layers quantized to Q8_0.
Qwen3-8B-abliterated-q4_k.gguf
- Output & embeddings quantized to Q8_0.
- All other layers quantized to Q4_K.
- Good for CPU inference with limited memory.
Qwen3-8B-abliterated-q4_k_s.gguf
- Smallest Q4_K variant, using less memory at the cost of accuracy.
- Best for very low-memory setups.
Qwen3-8B-abliterated-q6_k.gguf
- Output & embeddings quantized to Q8_0.
- All other layers quantized to Q6_K .
Qwen3-8B-abliterated-q8_0.gguf
- Fully Q8 quantized model for better accuracy.
- Requires more memory but offers higher precision.
Qwen3-8B-abliterated-iq3_xs.gguf
- IQ3_XS quantization, optimized for extreme memory efficiency.
- Best for ultra-low-memory devices.
Qwen3-8B-abliterated-iq3_m.gguf
- IQ3_M quantization, offering a medium block size for better accuracy.
- Suitable for low-memory devices.
Qwen3-8B-abliterated-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
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