* k_cache: be able to use Q5_0
* k_cache: be able to use Q5_1 on CODA
* k_cache: be able to use Q5_0 on Metal
* k_cache: be able to use Q5_1 on Metal
* k_cache: be able to use IQ4_NL - just CUDA for now
* k_cache: be able to use IQ4_NL on Metal
* k_cache: add newly added supported types to llama-bench and CUDA supports_op
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* server tests : remove seemingly redundant newlines in print()
* server tests : use built-in subprocess features, not os.kill and psutil
* server tests : do not catch e.g. SystemExit; use print_exc
* server tests: handle TimeoutExpired exception
* server tests: fix connect on dual-stack systems
* server: tests: add new tokens regex on windows generated following new repeat penalties default changed in (#6127)
* server: tests: remove the hack on windows since now we get the good socket family
* server: tests: add new tokens regex following new repeat penalties default changed in (#6127)
* server: tests: add new tokens regex following new repeat penalties default changed in (#6127)
---------
Co-authored-by: Pierrick HYMBERT <pierrick.hymbert@gmail.com>
* gguf-split: split and merge gguf files per tensor
* gguf-split: build with make toolchain
* gguf-split: rename `--split-tensors-size` to `--split-max-tensors`. Set general.split_count KV to all split
* split : minor style + fix compile warnings
* gguf-split: remove --upload not implemented
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* backend : offload large batches to GPU
* fix hip
* code cleanup
* fix CUDA split buffers
* Update ggml-backend-impl.h
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* cuda : fix memset without set_device
* imatrix : remove sched affix from weight names
* sched : add a new split if the current one has too many inputs
reduce max inputs per split
more cleanup
* update backends
ggml-ci
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* gritlm: add initial README.md to examples/gritlm
This commit adds a suggestion for an initial README.md for the gritlm
example.
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* squash! gritlm: add initial README.md to examples/gritlm
Use the `scripts/hf.sh` script to download the model file.
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* squash! gritlm: add initial README.md to examples/gritlm
Fix editorconfig-checker error in examples/gritlm/README.md.
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
---------
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* control vector api and implementation
* control-vectors : minor code style updates
* disable control vector when data == nullptr
use -1 for disabled range (also on init) in case we ever support controlling layer 0 (embeddings)
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Information about the Command-R 35B model (128k context) can be found at:
https://huggingface.co/CohereForAI/c4ai-command-r-v01
Based on the llama2 model with a few changes:
1) New hyper parameter to scale output logits (logit_scale)
2) Uses LayerNorm instead of RMSNorm
3) Transfomer layers have a single shared LayerNorm that feeds into both the
self-attention and FFN layers in parallel. There is no post-attention LayerNorm.
4) No support for Rotary Position Embeddings (RoPE) scaling
5) No biases used
Find GGUF files here:
https://huggingface.co/andrewcanis/c4ai-command-r-v01-GGUF
To convert model to GGUF format yourself:
1) Download Command-R Hugging Face safetensors:
git lfs install
git clone https://huggingface.co/CohereForAI/c4ai-command-r-v01
2) Run:
python3 convert-hf-to-gguf.py --outtype f16 ./c4ai-command-r-v01
* gguf : add support for I64 and F64 arrays
GGML currently does not support I64 or F64 arrays and they are not often
used in machine learning, however if in the future the need arises, it
would be nice to add them now, so that the types are next to the other
types I8, I16, I32 in the enums, and it also reserves their type number.
Furthermore, with this addition the GGUF format becomes very usable for
most computational applications of NumPy (being compatible with the most
common NumPy dtypes: i8, i16, i32, i64, f32, f64), providing a faster,
and more versatile alternative to the `npz` format, and a simpler
alternative to the `hdf5` format.
The change in this PR seems small, not significantly increasing the
maintenance burden. I tested this from Python using GGUFWriter/Reader
and `gguf-dump`, as well as from C, everything seems to work.
* Fix compiler warnings
