README.md · magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF at main

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	---
	license: apache-2.0
	tags:
	- mxfp4_hybrid
	- gguf
	- text-generation
	- quantized
	- cpu
	- gpu
	- mxfp4
	- mxfp4_moe
	- magicquant
	- magic_quant
	- IQ4_NL
	base_model:
	- unsloth/Seed-OSS-36B-Instruct
	---

	# MagicQuant GGUF Hybrids - Seed OSS 36B Instruct

	> MagicQuant is an automated quantization, benchmarking, and evolutionary hybrid-GGUF search system for LLMs.

	Each release includes models optimized to outperform standard baseline quants (Q8, Q6, Q5, Q4).
	If a baseline GGUF exists in this repo, the evolutionary engine couldn’t beat it.
	If a baseline is missing, it’s because a hybrid configuration outperformed it so completely that including the baseline would've been pointless.

	These hybrid GGUFs are built to be as small, fast, and low-drift as possible while preserving model capability.

	To dive deeper into how MagicQuant works, see the main repo:
	[MagicQuant on GitHub (by MagicCodingMan)](https://github.com/magiccodingman/MagicQuant-Wiki)

	Notes:
	* The HuggingFace hardware compatibility where it shows the bits is usually wrong. It doesn't understand hybrid mixes, so don't trust it.
	* Naming scheme can be found on the MagicQuant Wiki.
	* (tips) Less precision loss means less brain damage. More TPS means faster! Smaller is always better right?

	Precision Loss Guide

	* 0–0.1% → God-tier, scientifically exact
	* 0.1–1% → True near-lossless, agent-ready
	* 1–3% → Minimal loss, great for personal use
	* 3–5% → Borderline, but still functional
	* 5%+ → Toys, not tools, outside MagicQuant’s scope

	[Learn more about precision loss here](https://github.com/magiccodingman/MagicQuant-Wiki/blob/main/docs/precision-loss-guide.md).
	### Table - File Size + TPS + Avg Precision Loss

	\| model_name \| file_size_gb \| bench_tps \| avg_prec_loss \|
	\| ------------------------------------------------------------------------------------------------------------------------------------------------ \| ------------ \| --------- \| ------------- \|
	\| [mxfp4_moe-HK-B16-EO-Q5K-QUD-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-HK-B16-EO-Q5K-QUD-Q8_0.gguf?download=true) \| 39.71 \| 17.73 \| 0.0213% \|
	\| [mxfp4_moe-O-MXFP4-EHQKUD-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-O-MXFP4-EHQKUD-Q8_0.gguf?download=true) \| 35.78 \| 18.72 \| 0.0272% \|
	\| [mxfp4_moe-E-B16-D-IQ4NL-KOU-Q6K-HQ-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-E-B16-D-IQ4NL-KOU-Q6K-HQ-Q8_0.gguf?download=true) \| 28.02 \| 24.27 \| 0.1768% \|
	\| [mxfp4_moe-EHQKOUD-Q6K](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-EHQKOUD-Q6K.gguf?download=true) \| 27.63 \| 23.34 \| 0.2037% \|
	\| [mxfp4_moe-EHQKOUD-IQ4NL](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-EHQKOUD-IQ4NL.gguf?download=true) \| 18.95 \| 32.00 \| 0.2709% \|
	\| [mxfp4_moe-HQKU-IQ4NL-EOD-MXFP4](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-HQKU-IQ4NL-EOD-MXFP4.gguf?download=true) \| 18.66 \| 26.90 \| 0.7098% \|
	\| [MXFP4_MOE](./../../resolve/main/Seed-OSS-36B-Instruct-MXFP4_MOE.gguf?download=true) \| 17.90 \| 20.46 \| 2.7338% \|

	### Table - PPL Columns

	\| model_name \| gen \| gen_er \| code \| code_er \| math \| math_er \|
	\| ------------------------------------------------------------------------------------------------------------------------------------------------ \| ------ \| ------ \| ------ \| ------- \| ------ \| ------- \|
	\| [mxfp4_moe-HK-B16-EO-Q5K-QUD-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-HK-B16-EO-Q5K-QUD-Q8_0.gguf?download=true) \| 6.8901 \| 0.1680 \| 1.4127 \| 0.0095 \| 5.4434 \| 0.1208 \|
	\| [mxfp4_moe-O-MXFP4-EHQKUD-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-O-MXFP4-EHQKUD-Q8_0.gguf?download=true) \| 6.8866 \| 0.1679 \| 1.4130 \| 0.0095 \| 5.4474 \| 0.1210 \|
	\| [mxfp4_moe-E-B16-D-IQ4NL-KOU-Q6K-HQ-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-E-B16-D-IQ4NL-KOU-Q6K-HQ-Q8_0.gguf?download=true) \| 6.8901 \| 0.1682 \| 1.4156 \| 0.0096 \| 5.4284 \| 0.1203 \|
	\| [mxfp4_moe-EHQKOUD-Q6K](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-EHQKOUD-Q6K.gguf?download=true) \| 6.9012 \| 0.1685 \| 1.4135 \| 0.0095 \| 5.4637 \| 0.1218 \|
	\| [mxfp4_moe-EHQKOUD-IQ4NL](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-EHQKOUD-IQ4NL.gguf?download=true) \| 6.8712 \| 0.1654 \| 1.4162 \| 0.0095 \| 5.4627 \| 0.1201 \|
	\| [mxfp4_moe-HQKU-IQ4NL-EOD-MXFP4](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-HQKU-IQ4NL-EOD-MXFP4.gguf?download=true) \| 6.8452 \| 0.1639 \| 1.4140 \| 0.0094 \| 5.5223 \| 0.1222 \|
	\| [MXFP4_MOE](./../../resolve/main/Seed-OSS-36B-Instruct-MXFP4_MOE.gguf?download=true) \| 7.1007 \| 0.1728 \| 1.4351 \| 0.0097 \| 5.6360 \| 0.1239 \|

	### Table - Precision Loss Columns

	\| model_name \| loss_general \| loss_code \| loss_math \|
	\| ------------------------------------------------------------------------------------------------------------------------------------------------ \| ------------ \| --------- \| --------- \|
	\| [mxfp4_moe-HK-B16-EO-Q5K-QUD-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-HK-B16-EO-Q5K-QUD-Q8_0.gguf?download=true) \| 0.0421 \| 0.0071 \| 0.0147 \|
	\| [mxfp4_moe-O-MXFP4-EHQKUD-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-O-MXFP4-EHQKUD-Q8_0.gguf?download=true) \| 0.0087 \| 0.0142 \| 0.0588 \|
	\| [mxfp4_moe-O-IQ4NL-EHQKUD-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-O-IQ4NL-EHQKUD-Q8_0.gguf?download=true) \| 0.0087 \| 0.0142 \| 0.0588 \|
	\| [mxfp4_moe-E-B16-D-IQ4NL-KOU-Q6K-HQ-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-E-B16-D-IQ4NL-KOU-Q6K-HQ-Q8_0.gguf?download=true) \| 0.0421 \| 0.1982 \| 0.2902 \|
	\| [mxfp4_moe-EHQKOUD-Q6K](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-EHQKOUD-Q6K.gguf?download=true) \| 0.2033 \| 0.0495 \| 0.3582 \|
	\| [mxfp4_moe-EHQKOUD-IQ4NL](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-EHQKOUD-IQ4NL.gguf?download=true) \| 0.2323 \| 0.2407 \| 0.3398 \|
	\| [mxfp4_moe-HQKU-IQ4NL-EOD-MXFP4](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-HQKU-IQ4NL-EOD-MXFP4.gguf?download=true) \| 0.6098 \| 0.0849 \| 1.4346 \|
	\| [MXFP4_MOE](./../../resolve/main/Seed-OSS-36B-Instruct-MXFP4_MOE.gguf?download=true) \| 3.1000 \| 1.5784 \| 3.5230 \|


	---

	### Baseline Models (Reference)

	### Table - File Size + TPS + Avg Precision Loss

	\| model_name \| file_size_gb \| bench_tps \| avg_prec_loss \|
	\| ---------- \| ------------ \| --------- \| ------------- \|
	\| BF16 \| 67.35 \| 11.48 \| 0.0000% \|
	\| Q8_0 \| 35.78 \| 17.77 \| 0.0272% \|
	\| Q6_K \| 27.63 \| 22.95 \| 0.2037% \|
	\| Q5_K \| 23.84 \| 22.04 \| 0.2923% \|
	\| IQ4_NL \| 19.31 \| 27.70 \| 1.1076% \|
	\| MXFP4_MOE \| 17.90 \| 20.46 \| 2.7338% \|
	\| Q4_K_M \| 20.27 \| 26.65 \| 2.9161% \|

	### Table - PPL Columns

	\| model_name \| gen \| gen_er \| code \| code_er \| math \| math_er \|
	\| ---------- \| --- \| ------ \| ---- \| ------- \| ---- \| ------- \|
	\| BF16 \| 6.8872 \| 0.1679 \| 1.4128 \| 0.0095 \| 5.4442 \| 0.1209 \|
	\| Q8_0 \| 6.8866 \| 0.1679 \| 1.4130 \| 0.0095 \| 5.4474 \| 0.1210 \|
	\| Q6_K \| 6.9012 \| 0.1685 \| 1.4135 \| 0.0095 \| 5.4637 \| 0.1218 \|
	\| Q5_K \| 6.9056 \| 0.1685 \| 1.4169 \| 0.0096 \| 5.4616 \| 0.1213 \|
	\| IQ4_NL \| 6.9599 \| 0.1703 \| 1.4235 \| 0.0097 \| 5.5264 \| 0.1235 \|
	\| MXFP4_MOE \| 7.1007 \| 0.1728 \| 1.4351 \| 0.0097 \| 5.6360 \| 0.1239 \|
	\| Q4_K_M \| 7.0970 \| 0.1760 \| 1.4235 \| 0.0098 \| 5.7134 \| 0.1305 \|

	### Table - Precision Loss Columns

	\| model_name \| loss_general \| loss_code \| loss_math \|
	\| ---------- \| ------------ \| --------- \| --------- \|
	\| BF16 \| 0.0000 \| 0.0000 \| 0.0000 \|
	\| Q8_0 \| 0.0087 \| 0.0142 \| 0.0588 \|
	\| Q6_K \| 0.2033 \| 0.0495 \| 0.3582 \|
	\| Q5_K \| 0.2672 \| 0.2902 \| 0.3196 \|
	\| IQ4_NL \| 1.0556 \| 0.7574 \| 1.5099 \|
	\| MXFP4_MOE \| 3.1000 \| 1.5784 \| 3.5230 \|
	\| Q4_K_M \| 3.0462 \| 0.7574 \| 4.9447 \|


	---
	## Support

	I’m a solo developer working full time for myself to achieve my dream, pouring nights and weekends into open protocols and tools that I hope make the world a little better. If you chip in, you're helping me keep the lights on while I keep shipping.

	[Click here to see ways to support](https://sayou.biz/support) - BTC, Paypal, GitHub sponsors.

	Or, just drop a like on the repo :)

	---
	license: apache-2.0
	tags:
	- mxfp4_hybrid
	- gguf
	- text-generation
	- quantized
	- cpu
	- gpu
	- mxfp4
	- mxfp4_moe
	- magicquant
	- magic_quant
	- IQ4_NL
	base_model:
	- unsloth/Seed-OSS-36B-Instruct
	---

	# MagicQuant GGUF Hybrids - Seed OSS 36B Instruct

	> MagicQuant is an automated quantization, benchmarking, and evolutionary hybrid-GGUF search system for LLMs.

	Each release includes models optimized to outperform standard baseline quants (Q8, Q6, Q5, Q4).
	If a baseline GGUF exists in this repo, the evolutionary engine couldn’t beat it.
	If a baseline is missing, it’s because a hybrid configuration outperformed it so completely that including the baseline would've been pointless.

	These hybrid GGUFs are built to be as small, fast, and low-drift as possible while preserving model capability.

	To dive deeper into how MagicQuant works, see the main repo:
	[MagicQuant on GitHub (by MagicCodingMan)](https://github.com/magiccodingman/MagicQuant-Wiki)

	Notes:
	* The HuggingFace hardware compatibility where it shows the bits is usually wrong. It doesn't understand hybrid mixes, so don't trust it.
	* Naming scheme can be found on the MagicQuant Wiki.
	* (tips) Less precision loss means less brain damage. More TPS means faster! Smaller is always better right?

	Precision Loss Guide

	* 0–0.1% → God-tier, scientifically exact
	* 0.1–1% → True near-lossless, agent-ready
	* 1–3% → Minimal loss, great for personal use
	* 3–5% → Borderline, but still functional
	* 5%+ → Toys, not tools, outside MagicQuant’s scope

	[Learn more about precision loss here](https://github.com/magiccodingman/MagicQuant-Wiki/blob/main/docs/precision-loss-guide.md).
	### Table - File Size + TPS + Avg Precision Loss

	\| model_name \| file_size_gb \| bench_tps \| avg_prec_loss \|
	\| ------------------------------------------------------------------------------------------------------------------------------------------------ \| ------------ \| --------- \| ------------- \|
	\| [mxfp4_moe-HK-B16-EO-Q5K-QUD-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-HK-B16-EO-Q5K-QUD-Q8_0.gguf?download=true) \| 39.71 \| 17.73 \| 0.0213% \|
	\| [mxfp4_moe-O-MXFP4-EHQKUD-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-O-MXFP4-EHQKUD-Q8_0.gguf?download=true) \| 35.78 \| 18.72 \| 0.0272% \|
	\| [mxfp4_moe-E-B16-D-IQ4NL-KOU-Q6K-HQ-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-E-B16-D-IQ4NL-KOU-Q6K-HQ-Q8_0.gguf?download=true) \| 28.02 \| 24.27 \| 0.1768% \|
	\| [mxfp4_moe-EHQKOUD-Q6K](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-EHQKOUD-Q6K.gguf?download=true) \| 27.63 \| 23.34 \| 0.2037% \|
	\| [mxfp4_moe-EHQKOUD-IQ4NL](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-EHQKOUD-IQ4NL.gguf?download=true) \| 18.95 \| 32.00 \| 0.2709% \|
	\| [mxfp4_moe-HQKU-IQ4NL-EOD-MXFP4](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-HQKU-IQ4NL-EOD-MXFP4.gguf?download=true) \| 18.66 \| 26.90 \| 0.7098% \|
	\| [MXFP4_MOE](./../../resolve/main/Seed-OSS-36B-Instruct-MXFP4_MOE.gguf?download=true) \| 17.90 \| 20.46 \| 2.7338% \|

	### Table - PPL Columns

	\| model_name \| gen \| gen_er \| code \| code_er \| math \| math_er \|
	\| ------------------------------------------------------------------------------------------------------------------------------------------------ \| ------ \| ------ \| ------ \| ------- \| ------ \| ------- \|
	\| [mxfp4_moe-HK-B16-EO-Q5K-QUD-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-HK-B16-EO-Q5K-QUD-Q8_0.gguf?download=true) \| 6.8901 \| 0.1680 \| 1.4127 \| 0.0095 \| 5.4434 \| 0.1208 \|
	\| [mxfp4_moe-O-MXFP4-EHQKUD-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-O-MXFP4-EHQKUD-Q8_0.gguf?download=true) \| 6.8866 \| 0.1679 \| 1.4130 \| 0.0095 \| 5.4474 \| 0.1210 \|
	\| [mxfp4_moe-E-B16-D-IQ4NL-KOU-Q6K-HQ-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-E-B16-D-IQ4NL-KOU-Q6K-HQ-Q8_0.gguf?download=true) \| 6.8901 \| 0.1682 \| 1.4156 \| 0.0096 \| 5.4284 \| 0.1203 \|
	\| [mxfp4_moe-EHQKOUD-Q6K](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-EHQKOUD-Q6K.gguf?download=true) \| 6.9012 \| 0.1685 \| 1.4135 \| 0.0095 \| 5.4637 \| 0.1218 \|
	\| [mxfp4_moe-EHQKOUD-IQ4NL](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-EHQKOUD-IQ4NL.gguf?download=true) \| 6.8712 \| 0.1654 \| 1.4162 \| 0.0095 \| 5.4627 \| 0.1201 \|
	\| [mxfp4_moe-HQKU-IQ4NL-EOD-MXFP4](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-HQKU-IQ4NL-EOD-MXFP4.gguf?download=true) \| 6.8452 \| 0.1639 \| 1.4140 \| 0.0094 \| 5.5223 \| 0.1222 \|
	\| [MXFP4_MOE](./../../resolve/main/Seed-OSS-36B-Instruct-MXFP4_MOE.gguf?download=true) \| 7.1007 \| 0.1728 \| 1.4351 \| 0.0097 \| 5.6360 \| 0.1239 \|

	### Table - Precision Loss Columns

	\| model_name \| loss_general \| loss_code \| loss_math \|
	\| ------------------------------------------------------------------------------------------------------------------------------------------------ \| ------------ \| --------- \| --------- \|
	\| [mxfp4_moe-HK-B16-EO-Q5K-QUD-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-HK-B16-EO-Q5K-QUD-Q8_0.gguf?download=true) \| 0.0421 \| 0.0071 \| 0.0147 \|
	\| [mxfp4_moe-O-MXFP4-EHQKUD-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-O-MXFP4-EHQKUD-Q8_0.gguf?download=true) \| 0.0087 \| 0.0142 \| 0.0588 \|
	\| [mxfp4_moe-O-IQ4NL-EHQKUD-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-O-IQ4NL-EHQKUD-Q8_0.gguf?download=true) \| 0.0087 \| 0.0142 \| 0.0588 \|
	\| [mxfp4_moe-E-B16-D-IQ4NL-KOU-Q6K-HQ-Q8_0](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-E-B16-D-IQ4NL-KOU-Q6K-HQ-Q8_0.gguf?download=true) \| 0.0421 \| 0.1982 \| 0.2902 \|
	\| [mxfp4_moe-EHQKOUD-Q6K](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-EHQKOUD-Q6K.gguf?download=true) \| 0.2033 \| 0.0495 \| 0.3582 \|
	\| [mxfp4_moe-EHQKOUD-IQ4NL](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-EHQKOUD-IQ4NL.gguf?download=true) \| 0.2323 \| 0.2407 \| 0.3398 \|
	\| [mxfp4_moe-HQKU-IQ4NL-EOD-MXFP4](./../../resolve/main/Seed-OSS-36B-Instruct-mxfp4_moe-HQKU-IQ4NL-EOD-MXFP4.gguf?download=true) \| 0.6098 \| 0.0849 \| 1.4346 \|
	\| [MXFP4_MOE](./../../resolve/main/Seed-OSS-36B-Instruct-MXFP4_MOE.gguf?download=true) \| 3.1000 \| 1.5784 \| 3.5230 \|


	---

	### Baseline Models (Reference)

	### Table - File Size + TPS + Avg Precision Loss

	\| model_name \| file_size_gb \| bench_tps \| avg_prec_loss \|
	\| ---------- \| ------------ \| --------- \| ------------- \|
	\| BF16 \| 67.35 \| 11.48 \| 0.0000% \|
	\| Q8_0 \| 35.78 \| 17.77 \| 0.0272% \|
	\| Q6_K \| 27.63 \| 22.95 \| 0.2037% \|
	\| Q5_K \| 23.84 \| 22.04 \| 0.2923% \|
	\| IQ4_NL \| 19.31 \| 27.70 \| 1.1076% \|
	\| MXFP4_MOE \| 17.90 \| 20.46 \| 2.7338% \|
	\| Q4_K_M \| 20.27 \| 26.65 \| 2.9161% \|

	### Table - PPL Columns

	\| model_name \| gen \| gen_er \| code \| code_er \| math \| math_er \|
	\| ---------- \| --- \| ------ \| ---- \| ------- \| ---- \| ------- \|
	\| BF16 \| 6.8872 \| 0.1679 \| 1.4128 \| 0.0095 \| 5.4442 \| 0.1209 \|
	\| Q8_0 \| 6.8866 \| 0.1679 \| 1.4130 \| 0.0095 \| 5.4474 \| 0.1210 \|
	\| Q6_K \| 6.9012 \| 0.1685 \| 1.4135 \| 0.0095 \| 5.4637 \| 0.1218 \|
	\| Q5_K \| 6.9056 \| 0.1685 \| 1.4169 \| 0.0096 \| 5.4616 \| 0.1213 \|
	\| IQ4_NL \| 6.9599 \| 0.1703 \| 1.4235 \| 0.0097 \| 5.5264 \| 0.1235 \|
	\| MXFP4_MOE \| 7.1007 \| 0.1728 \| 1.4351 \| 0.0097 \| 5.6360 \| 0.1239 \|
	\| Q4_K_M \| 7.0970 \| 0.1760 \| 1.4235 \| 0.0098 \| 5.7134 \| 0.1305 \|

	### Table - Precision Loss Columns

	\| model_name \| loss_general \| loss_code \| loss_math \|
	\| ---------- \| ------------ \| --------- \| --------- \|
	\| BF16 \| 0.0000 \| 0.0000 \| 0.0000 \|
	\| Q8_0 \| 0.0087 \| 0.0142 \| 0.0588 \|
	\| Q6_K \| 0.2033 \| 0.0495 \| 0.3582 \|
	\| Q5_K \| 0.2672 \| 0.2902 \| 0.3196 \|
	\| IQ4_NL \| 1.0556 \| 0.7574 \| 1.5099 \|
	\| MXFP4_MOE \| 3.1000 \| 1.5784 \| 3.5230 \|
	\| Q4_K_M \| 3.0462 \| 0.7574 \| 4.9447 \|


	---
	## Support

	I’m a solo developer working full time for myself to achieve my dream, pouring nights and weekends into open protocols and tools that I hope make the world a little better. If you chip in, you're helping me keep the lights on while I keep shipping.

	[Click here to see ways to support](https://sayou.biz/support) - BTC, Paypal, GitHub sponsors.

	Or, just drop a like on the repo :)