Add missing GGUF quantization types (TQ1_0, TQ2_0, MXFP4) & move legacy types to the bottom (#2191)

* Add missing GGUF quantization types (TQ1_0, TQ2_0, MXFP4)

Sync the quantization types table with the source of truth at
huggingface.js/packages/gguf/src/quant-descriptions.ts:
- Add TQ1_0 and TQ2_0 (ternary quantization)
- Add MXFP4 (4-bit Microscaling Block Floating Point)
- Fix missing period on Q8_1 description

* Move legacy quantization types to bottom of table

Reorganize the table to place legacy types (Q8_0, Q8_1, Q5_0, Q5_1,
Q4_0, Q4_1) at the bottom with a separator row for better clarity.

Author: mishig25

docs/hub/gguf.md

@@ -72,16 +72,10 @@ Find more information [here](https://github.com/huggingface/huggingface.js/tree/
 | F16  | [Wikipedia](https://en.wikipedia.org/wiki/Half-precision_floating-point_format) | 16-bit standard IEEE 754 half-precision floating-point number. |
 | BF16 | [Wikipedia](https://en.wikipedia.org/wiki/Bfloat16_floating-point_format) | 16-bit shortened version of the 32-bit IEEE 754 single-precision floating-point number. |
 | I16 | [GH](https://github.com/ggerganov/llama.cpp/pull/6045) | 16-bit fixed-width integer number. |
-| Q8_0 | [GH](https://github.com/huggingface/huggingface.js/pull/615#discussion_r1557654249) | 8-bit round-to-nearest quantization (`q`). Each block has 32 weights. Weight formula: `w = q * block_scale`. Legacy quantization method (not used widely as of today). |
-| Q8_1 | [GH](https://github.com/huggingface/huggingface.js/pull/615#discussion_r1557682290) | 8-bit round-to-nearest quantization (`q`). Each block has 32 weights. Weight formula: `w = q * block_scale + block_minimum`. Legacy quantization method (not used widely as of today) |
 | Q8_K | [GH](https://github.com/ggerganov/llama.cpp/pull/1684#issue-1739619305) | 8-bit quantization (`q`). Each block has 256 weights. Only used for quantizing intermediate results. All 2-6 bit dot products are implemented for this quantization type. Weight formula: `w = q * block_scale`. |
 | I8 | [GH](https://github.com/ggerganov/llama.cpp/pull/6045) | 8-bit fixed-width integer number. |
 | Q6_K | [GH](https://github.com/ggerganov/llama.cpp/pull/1684#issue-1739619305) | 6-bit quantization (`q`). Super-blocks with 16 blocks, each block has 16 weights. Weight formula: `w = q * block_scale(8-bit)`, resulting in 6.5625 bits-per-weight. |
-| Q5_0 | [GH](https://github.com/huggingface/huggingface.js/pull/615#discussion_r1557654249) | 5-bit round-to-nearest quantization (`q`). Each block has 32 weights. Weight formula: `w = q * block_scale`. Legacy quantization method (not used widely as of today). |
-| Q5_1 | [GH](https://github.com/huggingface/huggingface.js/pull/615#discussion_r1557682290) | 5-bit round-to-nearest quantization (`q`). Each block has 32 weights. Weight formula: `w = q * block_scale + block_minimum`. Legacy quantization method (not used widely as of today). |
 | Q5_K | [GH](https://github.com/ggerganov/llama.cpp/pull/1684#issue-1739619305) | 5-bit quantization (`q`). Super-blocks with 8 blocks, each block has 32 weights. Weight formula: `w = q * block_scale(6-bit) + block_min(6-bit)`, resulting in 5.5 bits-per-weight. |
-| Q4_0 | [GH](https://github.com/huggingface/huggingface.js/pull/615#discussion_r1557654249) | 4-bit round-to-nearest quantization (`q`). Each block has 32 weights. Weight formula: `w = q * block_scale`. Legacy quantization method (not used widely as of today). |
-| Q4_1 | [GH](https://github.com/huggingface/huggingface.js/pull/615#discussion_r1557682290) | 4-bit round-to-nearest quantization (`q`). Each block has 32 weights. Weight formula: `w = q * block_scale + block_minimum`. Legacy quantization method (not used widely as of today). |
 | Q4_K | [GH](https://github.com/ggerganov/llama.cpp/pull/1684#issue-1739619305) | 4-bit quantization (`q`). Super-blocks with 8 blocks, each block has 32 weights. Weight formula: `w = q * block_scale(6-bit) + block_min(6-bit)`, resulting in 4.5 bits-per-weight. |
 | Q3_K | [GH](https://github.com/ggerganov/llama.cpp/pull/1684#issue-1739619305) | 3-bit quantization (`q`). Super-blocks with 16 blocks, each block has 16 weights. Weight formula: `w = q * block_scale(6-bit)`, resulting in 3.4375 bits-per-weight. |
 | Q2_K | [GH](https://github.com/ggerganov/llama.cpp/pull/1684#issue-1739619305) | 2-bit quantization (`q`). Super-blocks with 16 blocks, each block has 16 weights. Weight formula: `w = q * block_scale(4-bit) + block_min(4-bit)`, resulting in 2.625 bits-per-weight. |
@@ -94,5 +88,15 @@ Find more information [here](https://github.com/huggingface/huggingface.js/tree/
 | IQ2_XS | [HF](https://huggingface.co/CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF/blob/main/README.md?code=true#L59-L70) | 2-bit quantization (`q`). Super-blocks with 256 weights. Weight `w` is obtained using `super_block_scale` & `importance matrix`, resulting in 2.31 bits-per-weight. |
 | IQ1_S | [HF](https://huggingface.co/CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF/blob/main/README.md?code=true#L59-L70) | 1-bit quantization (`q`). Super-blocks with 256 weights. Weight `w` is obtained using `super_block_scale` & `importance matrix`, resulting in 1.56 bits-per-weight. |
 | IQ1_M | [GH](https://github.com/ggerganov/llama.cpp/pull/6302) | 1-bit quantization (`q`). Super-blocks with 256 weights. Weight `w` is obtained using `super_block_scale` & `importance matrix`, resulting in 1.75 bits-per-weight. |
+| TQ1_0 | [GH](https://github.com/ggml-org/llama.cpp/pull/8151) | Ternary quantization. |
+| TQ2_0 | [GH](https://github.com/ggml-org/llama.cpp/pull/8151) | Ternary quantization. |
+| MXFP4 | [GH](https://github.com/ggml-org/llama.cpp/pull/15091) | 4-bit Microscaling Block Floating Point. |
+| **Legacy types** | | |
+| Q8_0 | [GH](https://github.com/huggingface/huggingface.js/pull/615#discussion_r1557654249) | 8-bit round-to-nearest quantization (`q`). Each block has 32 weights. Weight formula: `w = q * block_scale`. Legacy quantization method (not used widely as of today). |
+| Q8_1 | [GH](https://github.com/huggingface/huggingface.js/pull/615#discussion_r1557682290) | 8-bit round-to-nearest quantization (`q`). Each block has 32 weights. Weight formula: `w = q * block_scale + block_minimum`. Legacy quantization method (not used widely as of today). |
+| Q5_0 | [GH](https://github.com/huggingface/huggingface.js/pull/615#discussion_r1557654249) | 5-bit round-to-nearest quantization (`q`). Each block has 32 weights. Weight formula: `w = q * block_scale`. Legacy quantization method (not used widely as of today). |
+| Q5_1 | [GH](https://github.com/huggingface/huggingface.js/pull/615#discussion_r1557682290) | 5-bit round-to-nearest quantization (`q`). Each block has 32 weights. Weight formula: `w = q * block_scale + block_minimum`. Legacy quantization method (not used widely as of today). |
+| Q4_0 | [GH](https://github.com/huggingface/huggingface.js/pull/615#discussion_r1557654249) | 4-bit round-to-nearest quantization (`q`). Each block has 32 weights. Weight formula: `w = q * block_scale`. Legacy quantization method (not used widely as of today). |
+| Q4_1 | [GH](https://github.com/huggingface/huggingface.js/pull/615#discussion_r1557682290) | 4-bit round-to-nearest quantization (`q`). Each block has 32 weights. Weight formula: `w = q * block_scale + block_minimum`. Legacy quantization method (not used widely as of today). |
 
 *if there's any inaccuracy on the table above, please open a PR on [this file](https://github.com/huggingface/huggingface.js/blob/main/packages/gguf/src/quant-descriptions.ts).*