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4c4cb30736
* iq4_nl: squash commits for easier rebase * Basics (quantize, dequantize) * CUDA dequantize and dot product * Slightly faster CUDA dot product (120 t/s) * Switch to 6-bit scales * Scalar dot product * AVX2 dot product * ARM_NEON dot product * Works on metal, but still slow * Slightly better Metal dot product * Another small Metal improvement * Metal dot product is getting there * Faster CUDA dot product * Add 1/8 ffn_down layers as Q5_K when no imatrix has been provided * Report the actual bpw * Add _xs mix that is 4.05 bpw for non-MoE models * Remove IQ4_XS for now, slightly adjust kvalues_iq4nl * AVX2 dot product uses Q8_0 instead of Q8_K * Add to test-backend-ops * Minor fix * Also use use Q5_K for attn_output in MoE models * Fixes after merging latest master * Switching to blocks of 32 * AVX2 for blocks of 32 * Scaler dot product for blocks of 32 * ARM_NEON dot product for blocks of 32 * Metal kernels for blocks of 32 * Slightly faster Metal kernels * Resurrecting iq3_xs After all the experimentation, nothing was better than this. * Minor PPL improvement via a block scale fudge factor * Minor improvement via 3 neighbours * iq3_xs: working scalar and AVX2 dot products * iq3_xs: ARM_NEON dot product - works but extremely slow (10 t/s) * iq3_xs: working Metal implementation * Adding IQ3_M - IQ3_XS mix with mostly Q4_K * iiq3_xs: a 3.4375 bpw variant * iq3_xs: make CUDA work for new version * iq3_xs: make scalar and AVX2 work for new version * iq3_s: make ARM_NEON work with new version * iq3_xs: make new version work on metal Performance is very similar to Q3_K_S * iq3_xs: tiny Metal speed improvement * iq3_xs: tiny Metal speed improvement * Fix stupid warning * Q3_K_XS now uses a mix of IQ3_XS and IQ3_XXS * iq3_xs: rename to iq3_s * iq3_s: make tests pass * Move Q3_K_XS mix to 3.25 bpw * Attempt to fix failing tests * Another attempt to fix the Windows builds * Attempt to fix ROCm * ROCm again * iq3_s: partial fix for QK_K = 64 * iq3_s: make it work on metal for QK_K = 64 Pleasent surprise: the coding was super-block size independent, so all it took was to delete some QK_K == 256 guards. * Will this fix ROCm? --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com> |
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| .. | ||
| baby-llama | ||
| batched | ||
| batched-bench | ||
| batched.swift | ||
| beam-search | ||
| benchmark | ||
| convert-llama2c-to-ggml | ||
| embedding | ||
| export-lora | ||
| finetune | ||
| gguf | ||
| imatrix | ||
| infill | ||
| jeopardy | ||
| llama-bench | ||
| llama.android | ||
| llama.swiftui | ||
| llava | ||
| lookahead | ||
| lookup | ||
| main | ||
| main-cmake-pkg | ||
| parallel | ||
| passkey | ||
| perplexity | ||
| quantize | ||
| quantize-stats | ||
| save-load-state | ||
| server | ||
| simple | ||
| speculative | ||
| sycl | ||
| tokenize | ||
| train-text-from-scratch | ||
| alpaca.sh | ||
| base-translate.sh | ||
| chat-13B.bat | ||
| chat-13B.sh | ||
| chat-persistent.sh | ||
| chat-vicuna.sh | ||
| chat.sh | ||
| CMakeLists.txt | ||
| gpt4all.sh | ||
| json-schema-to-grammar.py | ||
| llama2-13b.sh | ||
| llama2.sh | ||
| llama.vim | ||
| llm.vim | ||
| make-ggml.py | ||
| Miku.sh | ||
| pydantic_models_to_grammar.py | ||
| pydantic-models-to-grammar-examples.py | ||
| reason-act.sh | ||
| server-llama2-13B.sh | ||