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)
✔ 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
MiMo-VL-7B-RL-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.
MiMo-VL-7B-RL-f16.gguf
Model weights stored in F16.
Use if your device supports FP16, especially if BF16 is not available.
MiMo-VL-7B-RL-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.
MiMo-VL-7B-RL-f16-q8_0.gguf
Output & embeddings remain in F16.
All other layers quantized to Q8_0.
MiMo-VL-7B-RL-q4_k.gguf
Output & embeddings quantized to Q8_0.
All other layers quantized to Q4_K.
Good for CPU inference with limited memory.
MiMo-VL-7B-RL-q4_k_s.gguf
Smallest Q4_K variant, using less memory at the cost of accuracy.
Best for very low-memory setups.
MiMo-VL-7B-RL-q6_k.gguf
Output & embeddings quantized to Q8_0.
All other layers quantized to Q6_K .
MiMo-VL-7B-RL-q8_0.gguf
Fully Q8 quantized model for better accuracy.
Requires more memory but offers higher precision.
MiMo-VL-7B-RL-iq3_xs.gguf
IQ3_XS quantization, optimized for extreme memory efficiency.
Best for ultra-low-memory devices.
MiMo-VL-7B-RL-iq3_m.gguf
IQ3_M quantization, offering a medium block size for better accuracy.
Suitable for low-memory devices.
MiMo-VL-7B-RL-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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TurboLLM (GPT-4o-mini)
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Final Word
I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.
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In this report, we share our efforts to build a compact yet powerful VLM, MiMo-VL-7B. MiMo-VL-7B comprises (1) a native resolution ViT encoder that preserves fine-grained visual details, (2) an MLP projector for efficient cross-modal alignment, and (3) our MiMo-7B language model, specifically optimized for complex reasoning tasks.
The development of MiMo-VL-7B involves two sequential training processes: (1) A four-stage pre-training phase, which includes projector warmup, vision-language alignment, general multi-modal pre-training, and long-context Supervised Fine-Tuning (SFT). This phase yields the MiMo-VL-7B-SFT model. (2) A subsequent post-training phase, where we introduce Mixed On-policy Reinforcement Learning (MORL), a novel framework that seamlessly integrates diverse reward signals spanning perception accuracy, visual grounding precision, logical reasoning capabilities, and human/AI preferences. This phase yields the MiMo-VL-7B-RL model.
We open-source MiMo-VL-7B series, including checkpoints of the SFT and RL model.
We believe this report along with the models will provide valuable insights to develop powerful reasoning VLMs that benefit the larger community.
🛤️ During this journey, we find
Incorporating high-quality, broad-coverage reasoning data from the pre-training stage is crucial for enhancing model performance
We curate high-quality reasoning data by identifying diverse queries, employing large reasoning models to regenerate responses with long CoT, and applying rejection sampling to ensure quality.
Rather than treating this as supplementary fine-tuning data, we incorporate substantial volumes of this synthetic reasoning data directly into the later pre-training stages, where extended training yields continued performance improvements without saturation.
Mixed On-policy Reinforcement Learning further enhances model performance, while achieving stable simultaneous improvements remains challenging
We apply RL across diverse capabilities, including reasoning, perception, grounding, and human preference alignment, spanning modalities including text, images, and videos. While this hybrid training approach further unlock model’s potential, interference across data domains remains a challenge.
In general visual-language understanding, MiMo-VL-7B models achieve state-of-the-art open-source results.
Reasoning Tasks
In multi-modal reasoning, both the SFT and RL models significantly outperform all compared open-source baselines across these benchmarks.
[!IMPORTANT]
Results marked with * are obtained using our evaluation framework.
Tasks with ${\dagger}$ are evaluated by GPT-4o.
GUI Tasks
MiMo-VL-7B-RL possess exceptional GUI understanding and grounding capabilities. As a general-purpose VL model, MiMo-VL achieves comparable or even superior performance to GUI-specialized models.
Elo Rating
With our in-house evaluation dataset and GPT-4o judgments, MiMo-VL-7B-RL achieves the highest Elo rating among all evaluated open-source vision-language models, ranking first across models spanning from 7B to 72B parameters.
IV. Deployment
The MiMo-VL-7B series maintain full compatibility with the Qwen2_5_VLForConditionalGeneration architecture for deployment and inference.
