* Updated Models Layout
- Added a models drawer
- Added downloading directly from Hugging Face
- Load custom models from local folder
- Delete models by swiping left
* trimmed trailing white space
* Updated Models Layout
* common : streamline the formatting of help
- Separate alternative parameters by a comma
- Do not indent `--version` differently
* Update common/common.cpp
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Restore intended k-quants quantization mixes for MoE models
* Update Q2_K_S values in the quantize tool
Still using LLaMA-v1 PPL values in the quant description
today does not make much sense. But let's leave this update
for another PR.
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* iq2_xs: basics
* iq2_xs: this should have been in the basics
* iq2_xs: CUDA and scalar CPU works
* iq2_xs: WIP Metal
* iq2_xs: Metal now works
* iq2_xs: working, but dog slow, ARM_NEON dot product
* iq2_xs: better ARM_NEON dot product
We are now at 19.5 t/s for TG-128 and 61 t/s for PP-512 when
running on the CPU.
* iq2_xs: AVX2 dot product - 19.5 t/s
* iq2_xs: faster AVX2 dit product
21.4 t/s for TG-128, 59.2 t/s for PP-512.
The latter is 2x compared to the previous version.
* iq2_xs: had forgotten to delete iq2-data.h
* Add llama enum for IQ2_XS
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* server: added support for multiple api keys, added loading api keys from file
* minor: fix whitespace
* added file error handling to --api-key-file, changed code to better
reflect current style
* server: update README.md for --api-key-file
---------
Co-authored-by: Michael Coppola <info@michaeljcoppola.com>
* added /health endpoint to the server
* added comments on the additional /health endpoint
* Better handling of server state
When the model is being loaded, the server state is `LOADING_MODEL`. If model-loading fails, the server state becomes `ERROR`, otherwise it becomes `READY`. The `/health` endpoint provides more granular messages now according to the server_state value.
* initialized server_state
* fixed a typo
* starting http server before initializing the model
* Update server.cpp
* Update server.cpp
* fixes
* fixes
* fixes
* made ServerState atomic and turned two-line spaces into one-line
* updated `server` readme to document the `/health` endpoint too
* used LOG_INFO after successful model loading
NULL can be an integer constant expression with the value zero, in this case the behavior would be undefined because of an incorrect type being passed to the variable arguments.
* added /health endpoint to the server
* added comments on the additional /health endpoint
* Better handling of server state
When the model is being loaded, the server state is `LOADING_MODEL`. If model-loading fails, the server state becomes `ERROR`, otherwise it becomes `READY`. The `/health` endpoint provides more granular messages now according to the server_state value.
* initialized server_state
* fixed a typo
* starting http server before initializing the model
* Update server.cpp
* Update server.cpp
* fixes
* fixes
* fixes
* made ServerState atomic and turned two-line spaces into one-line
* updated `server` readme to document the `/health` endpoint too
* added /health endpoint to the server
* added comments on the additional /health endpoint
* Better handling of server state
When the model is being loaded, the server state is `LOADING_MODEL`. If model-loading fails, the server state becomes `ERROR`, otherwise it becomes `READY`. The `/health` endpoint provides more granular messages now according to the server_state value.
* initialized server_state
* fixed a typo
* starting http server before initializing the model
* Update server.cpp
* Update server.cpp
* fixes
* fixes
* fixes
* made ServerState atomic and turned two-line spaces into one-line
This update categorizes models with 24 layers as MODEL_1B, ensuring compatibility with different Phi model variants without impacting existing Phi-2 model functionality.
Uses ggml functions instead of hardcoded names and adds support to quantize into the modern Q-K variants.
This is just the bare minimum to get k-types working - a more refined choice of types would be needed to get best quality on low quantizations.
I ran a few tests, it doesn't break anything I could notice and a Q6_K ViT works almost as well as Q8_0 but 3 times the inference speed.
