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Created Tensor Encoding Schemes (markdown)
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Tensor-Encoding-Schemes.md
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There isn't really a real writeup of all the mapping, but this should hopefully be a good central starting point if any maintainers needs some understanding of when each feature was added and the general specs of each. Updating it with more accurate information is greatly appreciated
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## Tensor Naming Scheme
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This is not definitive, but is helpful when reading sourcecode or console output to understand what each means typically.
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- `<Encoding>_<Variants>`
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- `<Encoding>` : This defines the most common encoding of individual weights in the model
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- Floating Point Formats:
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- `BF16`: [16-bit bfloat16](https://en.wikipedia.org/wiki/Bfloat16_floating-point_format) [Google Brain](https://en.wikipedia.org/wiki/Google_Brain) truncated form of 32-bit IEEE 754 (1 sign bit, 8 exponent bits, 7 fractional bits)
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- `F64`: [64-bit IEEE 754](https://en.wikipedia.org/wiki/Double-precision_floating-point_format) floats per weight (1 sign bit, 11 exponent bits, 52 fractional bits)
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- `F32`: [32-bit IEEE 754](https://en.wikipedia.org/wiki/Single-precision_floating-point_format) floats per weight (1 sign bit, 8 exponent bits, 23 fractional bits)
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- `F16`: [16-bit IEEE 754](https://en.wikipedia.org/wiki/Half-precision_floating-point_format) floats per weight (1 sign bit, 5 exponent bits, 10 fractional bits)
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- Integer formats:
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- `I<X>`: X bits per weight, where `X` could be `4` (for 4 bits) or `8` (for 8 bits) etc...
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- Quantized formats:
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- `Q<X>`: X bits per weight, where `X` could be `4` (for 4 bits) or `8` (for 8 bits) etc...
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- `KQ<X>` (or `Q<X>_K`) : k-quant based models. X bits per weight, where `X` could be `4` (for 4 bits) or `8` (for 8 bits) etc...
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- `IQ<X>`: i-quant based models. X bits per weight, where `X` could be `4` (for 4 bits) or `8` (for 8 bits) etc...
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- `<Variants>`: This represents different strategies of packing quantized weights into a gguf file. This is because we may want a mix of different bit sizes for weights of varying importance, or we may be encoding a general offset to a block or super-block. This may be omitted if trivial or initial attempt, refer to encoding scheme name table for details.
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## Tensor Scheme Mapping
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| Scheme | `ggml_ftype` C enumeration name | `ggml_type` C enum name | Bits/Weight | Data Type | Block Configuration | Quantized Weight Formula | Initial Commits Or Pull Request Sources (of `ggml_type`) |
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| -------- | ------------------------------- | ----------------------- | ----------- | ----------------------------- | ---------------------------------------------------------------------- | ----------------------------------------------- | ------------------------------------------------------------------------ |
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| F32 | GGML_FTYPE_ALL_F32 | GGML_TYPE_F32 | 32 | 32-bit IEEE 754 | - | - | [llama.cpp CM: Initial Release](https://github.com/ggerganov/llama.cpp/commit/26c084662903ddaca19bef982831bfb0856e8257) |
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| F16 | GGML_FTYPE_MOSTLY_F16 | GGML_TYPE_F16 | 16 | 16-bit IEEE 754 | - | - | [llama.cpp CM: Initial Release](https://github.com/ggerganov/llama.cpp/commit/26c084662903ddaca19bef982831bfb0856e8257) |
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| Q4_0 | GGML_FTYPE_MOSTLY_Q4_0 | GGML_TYPE_Q4_0 | 4 | round to nearest quantization | Each block has 32 weights | w = q * block_scale | [llama.cpp CM: Initial Release](https://github.com/ggerganov/llama.cpp/commit/26c084662903ddaca19bef982831bfb0856e8257) |
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| Q4_1 | GGML_FTYPE_MOSTLY_Q4_1 | GGML_TYPE_Q4_1 | 4 | round to nearest quantization | Each block has 32 weights | w = q * block_scale + block_minimum | [llama.cpp CM: Initial Release](https://github.com/ggerganov/llama.cpp/commit/26c084662903ddaca19bef982831bfb0856e8257) |
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| Q4_1_F16 | GGML_FTYPE_MOSTLY_Q4_1_SOME_F16 | - | 4 | round to nearest quantization | Each block has 32 weights (token embedding and output weights are F16) | w = q * block_scale + block_minimum | [llama.cpp CM](https://github.com/ggerganov/llama.cpp/commit/6bc4400e67e6bc4faad3ad3d5e9d8a6576a9752d) |
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| Q8_0 | GGML_FTYPE_MOSTLY_Q8_0 | GGML_TYPE_Q8_0 | 8 | round to nearest quantization | Each block has 32 weights | w = q * block_scale | [llama.cpp PR: Add Q8_0 quantization format (rename the old one to Q8_1) (ARM NEON) #1179](https://github.com/ggerganov/llama.cpp/pull/1179) |
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| Q8_1 | - | GGML_TYPE_Q8_1 | 8 | round to nearest quantization | Each block has 32 weights | w = q * block_scale + block_minimum | [llama.cpp PR: Add Q8_0 quantization for intermediate results #951 (Note: Renamed to Q8_1 in later commit)](https://github.com/ggerganov/llama.cpp/pull/951) |
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| Q5_0 | GGML_FTYPE_MOSTLY_Q5_0 | GGML_TYPE_Q5_0 | 5 | round to nearest quantization | Each block has 32 weights | w = q * block_scale | [llama.cpp PR: Add Q5_0 and Q5_1 quantization #1187](https://github.com/ggerganov/llama.cpp/pull/1187) |
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| Q5_1 | GGML_FTYPE_MOSTLY_Q5_1 | GGML_TYPE_Q5_1 | 5 | round to nearest quantization | Each block has 32 weights | w = q * block_scale + block_minimum | [llama.cpp PR: Add Q5_0 and Q5_1 quantization #1187](https://github.com/ggerganov/llama.cpp/pull/1187) |
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| KQ2 | GGML_FTYPE_MOSTLY_Q2_K | GGML_TYPE_Q2_K | 2.5625 | k-quantization | Superblocks with 16 blocks, each block has 16 weights | w = q * block_scale (4-bit) + block_min (4-bit) | [llama.cpp PR: k-quants #1684](https://github.com/ggerganov/llama.cpp/pull/1684) |
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| KQ3 | GGML_FTYPE_MOSTLY_Q3_K | GGML_TYPE_Q3_K | 3.4375 | k-quantization | Superblocks with 16 blocks, each block has 16 weights | w = q * block_scale (6-bit) | [llama.cpp PR: k-quants #1684](https://github.com/ggerganov/llama.cpp/pull/1684) |
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| KQ4 | GGML_FTYPE_MOSTLY_Q4_K | GGML_TYPE_Q4_K | 4.5 | k-quantization | Superblocks with 8 blocks, each block has 32 weights | w = q * block_scale (6-bit) + block_min (6-bit) | [llama.cpp PR: k-quants #1684](https://github.com/ggerganov/llama.cpp/pull/1684) |
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| KQ5 | GGML_FTYPE_MOSTLY_Q5_K | GGML_TYPE_Q5_K | 5.5 | k-quantization | Superblocks with 8 blocks, each block has 32 weights | w = q * block_scale (6-bit) + block_min (6-bit) | [llama.cpp PR: k-quants #1684](https://github.com/ggerganov/llama.cpp/pull/1684) |
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| KQ6 | GGML_FTYPE_MOSTLY_Q6_K | GGML_TYPE_Q6_K | 6.5625 | k-quantization | Superblocks with 16 blocks, each block has 16 weights | w = q * block_scale (8-bit) | [llama.cpp PR: k-quants #1684](https://github.com/ggerganov/llama.cpp/pull/1684) |
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| KQ8 | - | GGML_TYPE_Q8_K | 8.? | k-quantization | ? | w = ? | [llama.cpp PR: k-quants #1684](https://github.com/ggerganov/llama.cpp/pull/1684) |
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| IQ2_XXS | GGML_FTYPE_MOSTLY_IQ2_XXS | GGML_TYPE_IQ2_XXS | 2.0625 | i-quantization | Superblocks with 8 blocks, each block has 32 weights | w = func(superblock_scale, importance_matrix) | [llama.cpp PR: SOTA 2-bit quants #4773](https://github.com/ggerganov/llama.cpp/pull/4773) |
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| IQ2_XS | GGML_FTYPE_MOSTLY_IQ2_XS | GGML_TYPE_IQ2_XS | 2.31 | i-quantization | Superblocks with 16 blocks, each block has 16 weights | w = func(superblock_scale, importance_matrix) | [llama.cpp PR: SOTA 2-bit quants - part 2 #4856](https://github.com/ggerganov/llama.cpp/pull/4856) |
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| IQ3_XXS | GGML_FTYPE_MOSTLY_IQ3_XXS | GGML_TYPE_IQ3_XXS | 3.0625 | i-quantization | Superblocks with 8 blocks, each block has 32 weights | w = func(superblock_scale, importance_matrix) | [llama.cpp PR: SOTA 3-bit quants #5196](https://github.com/ggerganov/llama.cpp/pull/5196) |
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| IQ1_S | GGML_FTYPE_MOSTLY_IQ1_S | GGML_TYPE_IQ1_S | 1.5 | i-quantization | Superblocks with 8 blocks, each block has 32 weights | w = func(superblock_scale, importance_matrix) | [llama.cpp PR: 1.5 bit quantization #5453](https://github.com/ggerganov/llama.cpp/pull/5453) |
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| IQ4_NL | GGML_FTYPE_MOSTLY_IQ4_NL | GGML_TYPE_IQ4_NL | 4.5 | i-quantization | Superblocks with 16 blocks, each block has 16 weights | w = [non linear mapping of quants to weights] | [llama.cpp PR: IQ4_NL: 4-bit non-linear quants with blocks of 32 #5590](https://github.com/ggerganov/llama.cpp/pull/5590) |
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| IQ3_S | GGML_FTYPE_MOSTLY_IQ3_S | GGML_TYPE_IQ3_S | 3.4375 | i-quantization | ? | w = func(superblock_scale, importance_matrix) | [llama.cpp PR: IQ3_S: a much better alternative to Q3_K #5676](https://github.com/ggerganov/llama.cpp/pull/5676) |
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| IQ2_S | GGML_FTYPE_MOSTLY_IQ2_S | GGML_TYPE_IQ2_S | 2.5 | i-quantization | ? | w = func(superblock_scale, importance_matrix) | [llama.cpp PR: Adding IQ2_S and IQ2_M to complete coverage of the 2-3 bit quantization range #5721](https://github.com/ggerganov/llama.cpp/pull/5721) |
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| IQ4_XS | GGML_FTYPE_MOSTLY_IQ4_XS | GGML_TYPE_IQ4_XS | 4.25 | i-quantization | Superblocks with 8 blocks, each block has 32 weights | w = func(superblock_scale, importance_matrix) | [llama.cpp PR: IQ4_XS: a 4.25 bpw quantization #5747](https://github.com/ggerganov/llama.cpp/pull/5747) |
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| IQ1_M | GGML_FTYPE_MOSTLY_IQ1_M | GGML_TYPE_IQ1_M | 1.75 | i-quantization | Superblocks with 16 blocks, each block has 16 weights | w = func(superblock_scale, importance_matrix) | [llama.cpp PR: IQ1_M: 1.75 bpw quantization #6302](https://github.com/ggerganov/llama.cpp/pull/6302) |
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| BF16 | GGML_FTYPE_MOSTLY_BF16 | GGML_TYPE_BF16 | 16 | bfloat16 (trunc 32b IEEE754) | - | - | [llama.cpp PR: Introduce bfloat16 support #6412](https://github.com/ggerganov/llama.cpp/pull/6412) |
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