eaddario/Qwen3-30B-A3B-GGUF

Experimental layer-wise quantization of Qwen/Qwen3-30B-A3B

Using LLaMA C++ release b5580 for quantization.

Original model: Qwen/Qwen3-30B-A3B

From the original model creators:

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:

• Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios.
• Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
• Superior human preference alignment, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
• Expertise in agent capabilities, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
• Support of 100+ languages and dialects with strong capabilities for multilingual instruction following and translation.

PLEASE READ THIS BEFORE USING THESE EXPERIMENTAL VERSIONS!

An area of personal interest is finding ways to optimize the inference performance of LLMs when deployed in resource-constrained environments like commodity hardware, desktops, laptops, mobiles, edge devices, etc. There are many approaches to accomplish this, including architecture simplification and knowledge distillation, but my focus has been primarily on quantization and pruning.

The method used to produce these experimental versions is covered in Squeezing Tensor Bits: the quest for smaller LLMs, but at a high level it involves using a custom version of llama-imatrix and llama-quantize to identify influential tensors, and quantize the most important layers to higher bit precision and the less important to lower bits. This process was partly inspired by Dumitru's et al Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels.

As of version b5125 llama-quantize can now perform tensor-wide quantization (TWQ), whereby user-defined tensors are quantized at a specific level, or perform layer-wise quantization (LWQ) by selecting different quantization types per tensor/layer. For example, --tensor-type attn_v=q6_k will quantize all Attention Value tensors at q6_k (TWQ), and --tensor-type "\.([0-9]|1[01257]|31)\.attn_k=q4_k" will quantize Attention Key tensors on layers 0 to 9, 10, 11, 12, 15, 17 and 31 at q4_k, leaving the remaining layers at their default value (LWQ).

The modified version of llama-imatrix generates useful statistics to guide the tensor selection process, --show-statistics will display:

• Σ(Bias): the sum of all activations over the tensor (i.e. the Importance Scores)
• Min & Max: minimum and maximum activation values
• μ & σ: activations' mean and standard deviation
• % Active: proportion of elements whose average activation exceeds a very small threshold (1e-6). Helpful to determine how alive/dormant the tensor is during inference
• N: number of activations in the tensor
• Entropy: entropy of the activation distribution, in bits (standard Shannon entropy measurement)
• E (norm): Normalized entropy.
• ZD Score: z-score distribution as described in 3.1 Layer Importance Scores in the Layer-Wise Quantization paper
• CosSim: cosine similarity between same type tensors with respect to the previous layer (i.e. blk.7.attn_k and blk.6.attn_k)

Please note that statistics are calculated for each individial tensor and should be used to compare between tensors of the same type only. For example, assuming that attn_k in layer 10 has a higher influence during inference than attn_k in layer 7 because its Σ(Bias) is larger makes sense, whilst concluding the same between attn_k and ffn_down does not.

There’s a pull request to merge these changes back into the core llama.cpp project. This may or may not ever happen so, until then, the modified version will be available on GitHub.

For testing and comparison I use models produced by Unsloth (Daniel and Michael Han do some really advanced level stuff!) and Bartowski (see credits below) but if they don't provide versions of the required model, all tests and comparisons are done against naive quantizations obtained by simply running llama-quantize with no further optimization.

All experimental versions were generated using an appropriate imatrix created from calibration datasets available at eaddario/imatrix-calibration. At its core, an Importance Matrix (imatrix) is a table or, more broadly, a structured representation that scores the relative importance of different features or parameters in a machine learning model. It essentially quantifies the "impact" each feature has on a specific outcome, prediction, or relationship being modeled, and it helps to counterbalance the negative effects of quantization and pruning.

The process to generate these models is roughly as follows:

1. Convert the original model's tensors to GGUF F16*
2. Estimate the Perplexity score for the F16 model (baseline) using the wikitext-2-raw-v1 dataset, and save the logits
3. Generate an imatrix from selected calibration datasets
4. Determine tensor and layer Importance Score contribution using the modified version of llama-imatrix
5. Select an appropriate quant level for each tensor and quantize the model using llama-quantize
6. Calculate Perplexity, KL Divergence, ARC (Easy+Challenge), HellaSwag, MMLU, Truthful QA and WinoGrande scores for each quantized model
7. Keep versions with the best scores
8. Repeat until all desired quants are created. I find that quantizations below Q3/IQ3 are not fit for my purposes and therefore do not usually generate them, but happy to provide other quants on request.

*BF16 would be preferred, but Apple's GPUs don't support it yet, and therefore any operations are executed in the CPU, making it unacceptably slow. This is expected to change in the near term but until then, if you are using Apple kit avoid using any models tagged BF16

Models

Sizes (in GB)

Model	Bartowski	Unsltoth	Repo	Shrinkage
Qwen3-30B-A3B-IQ3_M	14.1	N/A	14.1	0.4%
Qwen3-30B-A3B-IQ3_S	12.7	N/A	14.0	-9.9%
Qwen3-30B-A3B-IQ4_NL	17.4	17.3	17.1	1.9%
Qwen3-30B-A3B-Q3_K_L	14.6	N/A	14.2	3.0%
Qwen3-30B-A3B-Q3_K_M	14.1	14.7	13.5	4.2%
Qwen3-30B-A3B-Q3_K_S	13.4	13.3	13.4	-0.1%
Qwen3-30B-A3B-Q4_K_M	18.6	18.6	17.1	8.2%
Qwen3-30B-A3B-Q4_K_S	18.0	17.5	17.0	5.8%
Qwen3-30B-A3B-Q5_K_M	21.7	21.7	20.4	6.0%
Qwen3-30B-A3B-Q5_K_S	21.1	21.1	20.4	3.4%
Qwen3-30B-A3B-Q6_K	25.1	25.1	25.4	-1.0%
Qwen3-30B-A3B-Q8_0	32.5	32.5	29.9	7.9%

Perplexity and KL Divergence scores

Model	μPPL	𝜌PPL	μKLD	RMS Δp
Qwen3-30B-A3B-IQ3_M	9.017740 ±0.071039	98.03%	0.084570 ±0.000613	9.063 ±0.057
Qwen3-30B-A3B-IQ3_S	9.304507 ±0.073844	97.34%	0.119941 ±0.000678	10.621 ±0.058
Qwen3-30B-A3B-IQ4_NL	8.575853 ±0.065850	98.90%	0.044574 ±0.000353	6.714 ±0.048
Qwen3-30B-A3B-Q3_K_L	8.937656 ±0.069484	97.83%	0.093809 ±0.000679	9.446 ±0.056
Qwen3-30B-A3B-Q3_K_M	8.887952 ±0.068543	97.66%	0.101450 ±0.000743	9.773 ±0.057
Qwen3-30B-A3B-Q3_K_S	9.009827 ±0.069394	97.27%	0.120640 ±0.000817	10.712 ±0.059
Qwen3-30B-A3B-Q4_K_M	8.823778 ±0.069962	99.13%	0.033649 ±0.000352	5.656 ±0.045
Qwen3-30B-A3B-Q4_K_M-bartowski	8.598264 ±0.067271	99.52%	0.016689 ±0.000197	4.182 ±0.044
Qwen3-30B-A3B-Q4_K_M-unsloth	8.715955 ±0.068797	99.37%	0.022466 ±0.000223	4.803 ±0.044
Qwen3-30B-A3B-Q4_K_S	8.714889 ±0.068305	99.05%	0.037506 ±0.000408	5.989 ±0.046
Qwen3-30B-A3B-Q5_K_M	8.626179 ±0.067739	99.55%	0.014586 ±0.000155	3.873 ±0.040
Qwen3-30B-A3B-Q5_K_S	8.659571 ±0.068177	99.48%	0.017705 ±0.000209	4.198 ±0.040
Qwen3-30B-A3B-Q6_K	8.481278 ±0.065886	99.72%	0.007536 ±0.000144	2.853 ±0.045
Qwen3-30B-A3B-Q8_0	8.483070 ±0.065984	99.76%	0.005461 ±0.000129	2.433 ±0.043
Qwen3-30B-A3B-F16	8.445938 ±0.065177	100%	N/A	N/A

ARC, HellaSwag, MMLU, Truthful QA and WinoGrande scores

Scores generated using llama-perplexity with 750 tasks per test, and a context size of 768 tokens.

For the test data used in the generation of these scores, follow the appropiate links: HellaSwag, ARC, MMLU, Truthful QA and WinoGrande

Model	ARC	HellaSwag	MMLU	Truthful QA	WinoGrande	Avg Score
Qwen3-30B-A3B-IQ3_M	65.3333 ±1.7389	71.87	41.4667 ±1.8002	31.2000 ±1.6929	72.6667 ±1.6284	56.51
Qwen3-30B-A3B-IQ3_S	64.8000 ±1.7451	69.20	40.4000 ±1.7930	30.2667 ±1.6787	72.4000 ±1.6334	55.41
Qwen3-30B-A3B-IQ4_NL	65.2000 ±1.7405	72.00	42.2667 ±1.8050	31.3333 ±1.6949	74.0000 ±1.6027	56.96
Qwen3-30B-A3B-Q3_K_L	63.8667 ±1.7553	70.27	40.0000 ±1.7900	30.8000 ±1.6869	74.4000 ±1.5947	55.87
Qwen3-30B-A3B-Q3_K_M	63.2000 ±1.7621	69.33	40.1333 ±1.7910	30.6667 ±1.6849	72.9333 ±1.6235	55.25
Qwen3-30B-A3B-Q3_K_S	64.4000 ±1.7496	69.33	40.2667 ±1.7920	29.8667 ±1.6723	72.5333 ±1.6309	55.28
Qwen3-30B-A3B-Q4_K_M	66.2667 ±1.7276	72.13	42.6667 ±1.8072	31.0667 ±1.6909	74.6667 ±1.5892	57.36
Qwen3-30B-A3B-Q4_K_M-bartowski	63.7333 ±1.7567	75.07	40.6667 ±1.7948	32.0000 ±1.7045	70.0000 ±1.6744	56.29
Qwen3-30B-A3B-Q4_K_M-unsloth	63.7333 ±1.7567	74.53	40.8000 ±1.7958	32.1333 ±1.7063	70.0000 ±1.6744	56.24
Qwen3-30B-A3B-Q4_K_S	63.4667 ±1.7594	74.53	40.2667 ±1.7920	32.9333 ±1.7172	70.2667 ±1.6702	56.29
Qwen3-30B-A3B-Q5_K_M	64.2667 ±1.7510	75.07	41.6000 ±1.8010	31.4667 ±1.6968	70.5333 ±1.6658	56.59
Qwen3-30B-A3B-Q5_K_S	64.1333 ±1.7525	74.93	42.0000 ±1.8034	31.6000 ±1.6988	71.4667 ±1.6500	56.83
Qwen3-30B-A3B-Q6_K	64.0000 ±1.7539	75.46	41.0667 ±1.7976	32.2667 ±1.7082	71.7333 ±1.6453	56.91
Qwen3-30B-A3B-Q8_0	64.0000 ±1.7539	75.73	40.4000 ±1.7930	31.8667 ±1.7026	70.8000 ±1.6614	56.56
Qwen3-30B-A3B-F16	64.6667 ±1.7466	76.80	41.6000 ±1.8010	32.5333 ±1.7119	70.8000 ±1.6614	57.28

Tokens per Second - Benchmarks

Scores generated using llama-bench. Naive (llama-quantize with no optimization) Q4_K_M quantization included for comparison.

model	size	params	backend	threads	test	t/s
Qwen3-30B-A3B-Q4_K_M	15.90 GiB	30.53 B	Metal,BLAS	12	pp512	1021.17 ±4.51
Qwen3-30B-A3B-Q4_K_M	15.90 GiB	30.53 B	Metal,BLAS	12	tg128	81.09 ±0.28
Qwen3-30B-A3B-Q4_K_M	15.90 GiB	30.53 B	Metal,BLAS	12	pp1024+tg1024	108.20 ±0.22
Qwen3-30B-A3B-Q4_K_M-bartowski	17.35 GiB	30.53 B	Metal,BLAS	12	pp512	1036.03 ±4.78
Qwen3-30B-A3B-Q4_K_M-bartowski	17.35 GiB	30.53 B	Metal,BLAS	12	tg128	79.71 ±0.38
Qwen3-30B-A3B-Q4_K_M-bartowski	17.35 GiB	30.53 B	Metal,BLAS	12	pp1024+tg1024	105.32 ±1.79
Qwen3-30B-A3B-Q4_K_M-unsloth	17.28 GiB	30.53 B	Metal,BLAS	12	pp512	1046.82 ±2.87
Qwen3-30B-A3B-Q4_K_M-unsloth	17.28 GiB	30.53 B	Metal,BLAS	12	tg128	82.74 ±0.32
Qwen3-30B-A3B-Q4_K_M-unsloth	17.28 GiB	30.53 B	Metal,BLAS	12	pp1024+tg1024	110.35 ±0.38

Metrics used

Perplexity: one of the key metrics used in NLP evaluation. It measures the quality of a language model by evaluating how well it predicts the next token given a particular sequence of words. A PPL of 1 indicates an exact match between predicted and actual, whereas values greater than one indicate a degree of "surprise" the generated token differs from the expected.

Kullback–Leibler (KL) Divergence: a statistical measure of how much a probability distribution differs from another. When quantizing models (or altering the original tensors in any way for that matter), the closest we can preserve the weights' probability distribution to the original model the better, thus the closest to 0 the better.

AI2 Reasoning Challenge (ARC): a benchmark to evaluate the ability of AI models to answer complex science questions that require logical reasoning beyond pattern matching.

HellaSwag: the Harder Endings, Longer contexts, and Low-shot Activities for Situations With Adversarial Generations (bit of a mouthful!) is a benchmark designed to test commonsense natural language inference. It requires the model to predict the most likely ending of a sentence.

MMLU: the Massive Multitask Language Understanding evaluates LLMs’ general knowledge and problem-solving abilities across 57 subjects, including elementary mathematics, US history, computer science, and law.

Truthful QA: evaluates how well LLMs generate truthful responses to questions. It identifies whether AI models can avoid generating false or misleading information, particularly in areas where human knowledge is prone to misconceptions.

Winogrande: based on the Winograd Schema Challenge, is a natural language understanding task requiring models to resolve ambiguities in sentences involving pronoun references.

Credits

A big Thank You! to Colin Kealty for the many contributions and for being one of the best sources of high quality quantized models available on Huggingface, and a really big Thank You! to Georgi Gerganov for his amazing work with llama.cpp and the ggml/gguf libraries.