In recent downloads of LLAMA2 dataset the norm_eps is set to 1e-06, this
leads to convert.py erroneously considering the model to be LLAMA1 and
setting the context to 2k tokens.
Fix it by extending the existing hack to also check for the 1e-06 value.
* Add detection code for avx
* Only check hardware when option is ON
* Modify per code review sugguestions
* Build locally will detect CPU
* Fixes CMake style to use lowercase like everywhere else
* cleanup
* fix merge
* linux/gcc version for testing
* msvc combines avx2 and fma into /arch:AVX2 so check for both
* cleanup
* msvc only version
* style
* Update FindSIMD.cmake
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Co-authored-by: Howard Su <howard0su@gmail.com>
Co-authored-by: Jeremy Dunn <jeremydunn123@gmail.com>
* Revert "cuda : add ROCM aliases for CUDA pool stuff (#3918)"
This reverts commit 629f917cd6.
* Revert "cuda : use CUDA memory pool with async memory allocation/deallocation when available (#3903)"
This reverts commit d6069051de.
ggml-ci
* Using cuda memory pools for async alloc/dealloc.
* If cuda device doesnt support memory pool than use old implementation.
* Removed redundant cublasSetStream
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Co-authored-by: Oleksii Maryshchenko <omaryshchenko@dtis.com>
* cmake : fix build when .git does not exist
* cmake : simplify BUILD_INFO target
* cmake : add missing dependencies on BUILD_INFO
* build : link against build info instead of compiling against it
* zig : make build info a .cpp source instead of a header
Co-authored-by: Matheus C. França <matheus-catarino@hotmail.com>
* cmake : revert change to CMP0115
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Co-authored-by: Matheus C. França <matheus-catarino@hotmail.com>
* Add '-ngl' support to finetune.cpp
* Add fprintf in ggml_cuda_op_add
When I tried CUDA offloading during finetuning following the readme, I got an assert here.
This probably isn't an important case because inference later gives a warning saying you should use f16 or f32 instead when using lora
* Add 'finetune.sh', which currently fails when using GPU
"error: operator (): Finetuning on tensors with type 'f16' is not yet supported"
* tweak finetune.sh
* Suppress some warnings in ggml.c
* Add f16 implementation to ggml_compute_forward_add_f16_f32
* Add an f16 case to ggml_add_cast_impl and llama_build_lora_finetune_graphs
* finetune.sh: Edit comments
* Add "add_f16_f32_f32_cuda"
* Tweak an error message
* finetune.sh: Add an optional LLAMA_MODEL_DIR variable
* finetune.sh: Add an optional LLAMA_TRAINING_DIR variable
* train : minor
* tabs to spaces
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>
* Introduce the new Min-P sampler by @kalomaze
The Min-P sampling method was designed as an alternative to Top-P, and aims to ensure a balance of quality and variety. The parameter *p* represents the minimum probability for a token to be considered, relative to the probability of the most likely token.
* Min-P enabled and set to 0.05 default
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>
* Extend llama_kv_cache_seq_rm to allow matichng any sequence
* Replace llama_kv_cache_tokens_rm with llama_kv_cache_clear
Use llama_kv_cache_clear for cache clearing
Change calls to llama_kv_cache_tokens_rm that want to delete by position to use llama_kv_cache_seq_rm functionality
