* 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
* iq4_nl: Fix after merging with master
* iq4_nl: another fix after merging with master
* Use IQ4_NL instead of Q4_K when using k-quants is not possible
* Fix typo that makes several tests fail
* It was the ggml_vdotq thing missed inside the brackets
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit contains a suggestion for the README.md in the llava
example. The suggestion adds explicit instructions for how to convert
a llava-1.6 model and run it using llava-cli.
The motivation for this is that having explicit instructions similar to
the 1.5 instructions will make it easier for users to try this out.
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* add build support for embedded metal library
* Update Makefile
---------
Co-authored-by: Haoxiang Fei <feihaoxiang@idea.edu.cn>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* support minLength and maxLength in JSON schema grammar converter
* Update examples/json-schema-to-grammar.py
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* ggml : embed Metal library source (ggml-metal.metal) into binary
enable by setting WHISPER_EMBED_METAL_LIBRARY
* rename the build option
* rename the preprocessor directive
* generate Metal library embedding assembly on-fly during build process
This is a follup of Commit fc0c8d286a
("llava : update surgery script to not remove tensors") but this time
the change is to the BakLLaVA specific part of the surgery script.
I've been able to test this using SkunkworksAI/BakLLaVA-1 and it works
as expected using the instructions in README.md.
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* llama: add llama_chat_apply_template
* test-chat-template: remove dedundant vector
* chat_template: do not use std::string for buffer
* add clarification for llama_chat_apply_template
* llama_chat_apply_template: add zephyr template
* llama_chat_apply_template: correct docs
* llama_chat_apply_template: use term "chat" everywhere
* llama_chat_apply_template: change variable name to "tmpl"
* #ifdef out some code NUMA blocks for Android due to lack of support
* added in some __ANDROID__ if def gates around numa code and forced GLIBC prior to 2.29 to use a syscall for getcpu instead of the wrapper
* Changed gates on numa platform specific stuff to __gnu_linux__ to skip any platforms without glibc
* harmonizing #if defined blocks for numa code to __gnu_linux__ since that's the only model that's being followed anyways
---------
Co-authored-by: root <root@nenya.lothlorien.ca>
* build : pass all warning flags to nvcc via -Xcompiler
* make : fix apparent mis-merge from #3952
* make : fix incorrect GF_CC_VER for CUDA host compiler
* server: enrich health endpoint with available slots, return 503 if not slots are available
* server: document new status no slot available in the README.md
This commit updates the surgery script to not remove the tensors from the
model file. For this to work the `--skip-unknown` flag is added as an
argument to the convert.py script in README.md.
The motivation for this change is that the surgery script currently
removes the projector tensors from the model file. If the model was
checked out from a repository, the model file will have been updated
and have to be checked out again to reset this effect. If this can be
avoided I think it would be preferable.
I did not perform this change for BakLLaVA models as I am not sure
how that part works.
* iq1_s: WIP basics
* iq1_s: CUDA is working
* iq1_s: scalar CPU dot product
* iq1_s: WIP AVX2 dot product - something is not right
* Fix tests
* Fix shadow warnings
* Fix after merge with latest master
* iq1_s: AVX2 finally works
* iq1_s: ARM_NEON dot product. Works, but not very fast
* iq1_s: better grid
* iq1_s: use IQ2_XXS for attn_output
At a cost of 0.04 extra bpw this gives a big improvement in PPL.
* iq1_s: Metal basics
Dequantize works, but not dot product
* iq1_s: Metal works, but quite slow
As usual, Apple Silicon does not like the code I write.
* iq1_s: Tests
* iq1_s: slightly faster dot product
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
