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2024-01-30kompute : llama-bench support and ggml_cpu_has_kompute() (#5226)Jared Van Bortel
2024-01-30SOTA 3-bit quants (#5196)Kawrakow
* iq3_xxs: quantize/dequantize RMSE seems a bit high-ish at about half-way between q2_K and q3_K, so need to check more. * iq3_xxs: CUDA dequantize works * iq2_xxs: tuning quantization * iq3_xxs: starting to look better PPL on wiki.test.raw LLaMA-v1-7B: 6.4218 LLaMA-v2-7B: 6.3560 Mistral-7B : 6.0717 This is better than Q3_K_XS, with a 5% reduction in quantized model size. * iq3_xxs: CUDA dot product We have PP-512: 5891 t/s TG-128: 143.9 t/s * iq3_xxs: scalar and AVX2 dot products * iq3_xxs: ARM_NEON and Metal Metal performance is decent, ARM_NEON is pathetic * iq3_xxs: slightly better grid points * Faster iq3_xxs and iq2_xs dot products on CUDA * iq3_xxs: add some quant mix * iq3_xxs: fix failing quantization test Dot product still fails. Is this real? * iq3_xxs: hopefully fix ROCm * iq3_xxs: failing tests This time the dot product accuracy did find an actual bug in the AVX2 implementation. * Add IQ3_XXS to test-backend-ops --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-01-29kompute : fix fallback to CPU (#5201)Jared Van Bortel
2024-01-29Nomic Vulkan backend (#4456)Jared Van Bortel
Signed-off-by: Jared Van Bortel <jared@nomic.ai> Co-authored-by: niansa <anton-sa@web.de> Co-authored-by: Adam Treat <treat.adam@gmail.com> Co-authored-by: Aaron Miller <apage43@ninjawhale.com> Co-authored-by: ToKiNoBug <tokinobug@163.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
2024-01-29fix typo "RLIMIT_MLOCK" (#5175)divinity76
2024-01-28ggml : add Vulkan backend (#2059)0cc4m
* Vulkan loader code * Fix matmul kernel, continue implementation * Continue implementation * Vulkan memory management * Vulkan development * Matmul call * Add aligned malloc and free for VMA * Continue implementation * First matmul success * GEMM Kernel optimization * 1D Blocktiling * 2D Blocktiling * Write coalescing * Continue vulkan implementation and optimization * First FP16 attempt, disabled for now * Code abstraction, FP16 implementation, fix kernel, add FP16 to FP32 kernel * Enable device extensions properly, restore fp16 matmul op * Fix mulmat_f16 * Output FP32 in fp16 matmul shader * Fix f16_to_f32 kernel * dequant_q4_0 kernel * Add VMA library * Avoid requesting dedicated memory, VMA can decide that by itself * Add bounds checking to matmul kernels, improve implementation, fix command buffers not freed properly * add cmake commands * Add 2d write operation, profiling code * Fix 2d write * Fix queue selection for AMD RADV * Fix trailing whitespace in vk_mem_alloc.h * Add WIP warp tile mat mul shaders * Disable glslc optimization * Disable glslc optimization for CMake * Optimize warptile matmul shader, replace blocktile with it * Add split-k optimization for small matrix multiplication Use semaphores for synchronization instead of fences or waitidle Rework async write/read for synchronization * Fix validation errors, improve compatibility with AMD GPUs * Rework command buffer handling * Variable matmul kernel using specialization constants * Fix synchronization on AMD, add barriers for buffer ownership transfer, add debug flag and prints * Reuse semaphores * Handle stage flags during command buffer submission properly * Increase matmul test runs for consistent results * Fix F32 matmul * Add vectorized loading and zeropadding for matrix multiplication * Use pinned memory for f16 preprocessing * Don't force aligned matmul * Don't free before queue done * Replace VMA library with native Vulkan buffer management * Basic offloading support with mul_f32 and dmmv for q4_0 * Run glslc commands in parallel * Unroll loops in dmmv shader * Reduce usage of waitIdle * Reuse pinned allocation for f16 conversion * Handle devices with only a single queue * Fix trailing whitespace in CMakeLists.txt * Allow parallel execution of kernels, parallelize third and fourth dimension calls * Add fallback for devices only supporting one DescriptorSet per DescriptorPool * Move to graph function similar to CUDA implementation * Use F16 kernel for most things, replace q_f32 with mul_mat_q_f16 function * Add F32 dmmv shaders * Batch submissions * Add .spv to gitignore * Split off matrix vector multiplication for separate optimization * Use single command buffer for matrix vector multiplication ops * Reduce overhead of mul_f32 calls by using a single command buffer * Add submission batching to mul_f32 * Fix tests * Add missing barrier * Add further missing barrier * Add further ops * Replace vk::QueueFamilyIgnored with VK_QUEUE_FAMILY_IGNORED to support more Vulkan header versions * Remove unnecessary cblas link * Fix descriptor set pre-allocation assert * Add runtime shader compilation, start transferring shaders to this approach * Transfer remaining shaders to header and compile on runtime * Fix fp32 fallback if device doesn't support fp16, add force disable env var GGML_VULKAN_DISABLE_F16 * Add support for q4_1, q5_0, q5_1 and q8_0 * Remove unnecessary scalar layout extension * Parse graph early to pre-record command buffers * Add q6_k support * Add multi-submit for command buffers * Fix q6_k dequant shader for AMD * Fix q6_k for GPUs without fp16 support * Simplify q6_k fp16 fix * Minor fixes * Fix wg_denom of m-mulmat shaders * Add Python-based Vulkan shader generator * Replace shaderc dependency with precompiled shaders Fix python script to generate shaders * Clean up code * Fix shader generator script Windows compatibility Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com> * Close file before deletion * Fix vulkan shader fp32 name * Add q2_k and q3_k support Add validation check to compare shader results to cpu results * Add q4_k support * Add q5_k support * Bake SPIR-V bytecode into the library instead of loading shaders from file * Switch to signal semaphores for flexibility Prepare broadcasting support for mul mat * Finish broadcasting mul mat support for GQA * Clean up unused functions Add repeat op * Add further ops, not yet enabled. Improve semaphore code * Reduce number of used semaphores by utilizing timelines more properly * Remove queue information * Reuse timeline semaphores, allow parallel operation with binary semaphores to work around nvidia driver limitations * Add Vulkan to llama-bench * Remove cblas dependency * Fix matmul k-split bug * Fix q4_k dmmv K_QUANTS_PER_ITERATION 1 shader * Add RMS Norm shader, rework op_f32 shader setup, fix matmul bug * Fix issues with float16 overflows in shaders * Fix issues with older Vulkan headers on Ubuntu 22.04 * Allow multi-op partial offloading by parsing the graph to preallocate enough between-op buffers * Implement further ops, rework op_f32 calls, fix bugs * Finish full offloading support, add last remaining ops, fix bugs, remove redundant code * Upload generated file ggml-vulkan-shaders.hpp, remove redundant shaders * Merge upstream changes, fix conflicts, adapt soft_max op * Fix Python and shader header format * Free model gpu buffers on exit * Use single queue per device to simplify code * Add matmul shader support for running multiple calculations in parallel * Switch from semaphore-synchronized multiple command buffers per op to single command buffer for multiple ops, whole graph if possible * Fix missing event cast * Replace uint64_t(-1) with UINT64_MAX, rename function for clarity * Fix warning about empty C function parameters * Fix compiler warnings * Properly implement Vulkan backend buffer handling * Fix oversized host staging buffers * Simplify barrier synchronization calls * Fix gcc warnings * Implement max_size for backend buffer types to limit the size of a single allocation * Use min of maxMemoryAllocationSize and maxBufferSize for device max allocation size * refactor multi buf * Disable unsupported ops to fix tests * Check for maintenance4 support before using it * Handle devices with only a single queue * Fix single queue logic * propagate buffer usage in multi buffers * Implement rope_neox op * Cleanup header and other files * Simplify gpu_extras by removing events and putting staging memcpys into contexts * Move queue into context Add not-yet-enabled async backend ops * Simplify context use, optimize matmul shader for warp size 64 (AMD GCN), fix split_k matmul shader optimization * Add get_max_size to SYCL backend. Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * llama : fix trailing whitespace --------- Co-authored-by: Henri Vasserman <henv@hot.ee> Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com> Co-authored-by: slaren <slarengh@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-28ggml : add unified SYCL backend for Intel GPUs (#2690)Abhilash Majumder
* first update for migration * update init_cublas * add debug functio, commit all help code * step 1 * step 2 * step3 add fp16, slower 31->28 * add GGML_LIST_DEVICE function * step 5 format device and print * step6, enhance error check, remove CUDA macro, enhance device id to fix none-zero id issue * support main device is non-zero * step7 add debug for code path, rm log * step 8, rename all macro & func from cuda by sycl * fix error of select non-zero device, format device list * ren ggml-sycl.hpp -> ggml-sycl.h * clear CMAKE to rm unused lib and options * correct queue: rm dtct:get_queue * add print tensor function to debug * fix error: wrong result in 658746bb26702e50f2c59c0e4ada8e9da6010481 * summary dpct definition in one header file to replace folder:dpct * refactor device log * mv dpct definition from folder dpct to ggml-sycl.h * update readme, refactor build script * fix build with sycl * set nthread=1 when sycl, increase performance * add run script, comment debug code * add ls-sycl-device tool * add ls-sycl-device, rm unused files * rm rear space * dos2unix * Update README_sycl.md * fix return type * remove sycl version from include path * restore rm code to fix hang issue * add syc and link for sycl readme * rm original sycl code before refactor * fix code err * add know issue for pvc hang issue * enable SYCL_F16 support * align pr4766 * check for sycl blas, better performance * cleanup 1 * remove extra endif * add build&run script, clean CMakefile, update guide by review comments * rename macro to intel hardware * editor config format * format fixes * format fixes * editor format fix * Remove unused headers * skip build sycl tool for other code path * replace tab by space * fix blas matmul function * fix mac build * restore hip dependency * fix conflict * ren as review comments * mv internal function to .cpp file * export funciton print_sycl_devices(), mv class dpct definition to source file * update CI/action for sycl code, fix CI error of repeat/dup * fix action ID format issue * rm unused strategy * enable llama_f16 in ci * fix conflict * fix build break on MacOS, due to CI of MacOS depend on external ggml, instead of internal ggml * fix ci cases for unsupported data type * revert unrelated changed in cuda cmake remove useless nommq fix typo of GGML_USE_CLBLAS_SYCL * revert hip cmake changes * fix indent * add prefix in func name * revert no mmq * rm cpu blas duplicate * fix no_new_line * fix src1->type==F16 bug. * pass batch offset for F16 src1 * fix batch error * fix wrong code * revert sycl checking in test-sampling * pass void as arguments of ggml_backend_sycl_print_sycl_devices * remove extra blank line in test-sampling * revert setting n_threads in sycl * implement std::isinf for icpx with fast math. * Update ci/run.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update examples/sycl/run-llama2.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update examples/sycl/run-llama2.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add copyright and MIT license declare * update the cmd example --------- Co-authored-by: jianyuzh <jianyu.zhang@intel.com> Co-authored-by: luoyu-intel <yu.luo@intel.com> Co-authored-by: Meng, Hengyu <hengyu.meng@intel.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-28Apply min_p to unsorted tokens (#5115)Johannes Gäßler
2024-01-28Tests for min_p, sampling queue (#5147)Johannes Gäßler
2024-01-28llama : add support for Orion-14B (#5118)sharpHL
* add support for Orion-14B(https://huggingface.co/OrionStarAI/Orion-14B-Chat) * flake8 support * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp Co-authored-by: slaren <slarengh@gmail.com> * Update llama.cpp * Update llama.cpp --------- Co-authored-by: lixiaopu <lixiaopu@cmcm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: slaren <slarengh@gmail.com>
2024-01-26Another bucket sort (#5109)Kawrakow
* Initial bucket sort * Bucket sort: slightly better version * Bucket sort: another minor improvement --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-01-25llama : dynamic temperature sampling (#4972)l3utterfly
* implemented dynamic temperature sampling from koboldcpp * removed trailing whitespace * removed unused temp parameter in llama_sample_entropy * exposed exponent_val in dynamic temp sampler * added debug check for printf statements * use nullptr in llama_sample_softmax call during llama_sample_entropy this avoids counting the time taken stats twice Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * return earlier if there is only 1 candiate (i.e. max_entropy == 0) * reformat 't' case in llama_sample_queue Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> * check for one or zero candidates case in llama_sample_entropy --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
2024-01-25Fix Q3_K_XS for MoE models (#5113)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-01-24llama : pre-allocate input tensors in a separate buffer (#5100)slaren
2024-01-23minor : clean-up some warnings and style (#5094)Georgi Gerganov
* minor : clean-up some warnings and style ggml-ci * ggml : add comment
2024-01-22llama : fix not enough space in buffer with Qwen (#5086)slaren
2024-01-22llama : support StableLM 2 1.6B (#5052)compilade
* llama : support StableLM 2 1.6B * convert : fix Qwen's set_vocab wrongly naming all special tokens [PAD{id}] * convert : refactor Qwen's set_vocab to use it for StableLM 2 too * nix : add tiktoken to llama-python-extra * convert : use presence of tokenizer.json to determine StableLM tokenizer loader It's a less arbitrary heuristic than the vocab size.
2024-01-22llama : add Q3_K_XS (#5060)Kawrakow
* Add Q3_K_XS - intermediate size between Q2_K and Q3_K_S * Q3_K_XS: quanize first 1/8 of ffn_down layers with Q4_K Together with an importance matrix, this brings perplexity for LLaMA-v2-70B below the perplexity of the former Q2_K with a 800 MB smaller quantized model size. --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-01-22llama : add more qwen2 models (#5071)Shijie
2024-01-20llama : run all KQV ops on the CPU with no KV offload (#5049)slaren
ggml-ci
2024-01-19llama : support upcoming Qwen2 (#5037)Shijie
2024-01-19llama : add CodeShell support (#5016)chiranko
* llama: add codeshell support * llama.cpp: fix codeshell with NeoX rope Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-19llama : fix falcon arch for tied output embeddings (#4978)John
* falcon arch fix for tied output embeddings * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp * Update llama.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update llama.cpp --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-18llama : fix mlock with no-mmap with Metal (#5025)slaren
2024-01-17ggml : add IQ2 to test-backend-ops + refactoring (#4990)Georgi Gerganov
* ggml : add IQ2 to test-backend-ops + refactoring ggml-ci * cuda : update supports_op for IQ2 ggml-ci * ci : enable LLAMA_CUBLAS=1 for CUDA nodes ggml-ci * cuda : fix out-of-bounds-access in `mul_mat_vec_q` ggml-ci * tests : avoid creating RNGs for each Q tensor ggml-ci * tests : avoid creating RNGs for each tensor ggml-ci
2024-01-17backend : add eval callback (#4935)Georgi Gerganov
* backend : add eval callback ggml-ci * backend : group nodes in a single compute when user don't need them * backend : clean-up the implementation ggml-ci * simple : do not perform tensor data copy if not needed * simple : fix * simple : no need for ggml_is_contiguous + fix bool parse * llama : fix callback placement in llama_context_params * backend : avoid double-ask callback calls * simple : restore examples, imatrix will serve as a demo
2024-01-17llama : use Q4_K for attn_v for Q2_K_S when n_gqa >= 4 (#4996)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-01-16ggml : importance matrix support for legacy quants (#4969)Kawrakow
* imatrix: adding support for legacy quants * imatrix: guard Q4_0/Q5_0 against ffn_down craziness --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-01-15llama : apply classifier-free guidance to logits directly (#4951)David Friehs
2024-01-15llama : check for 256 divisibility for IQ2_XS, IQ2_XXS (#4950)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-01-14llama : fix missing quotes (#4937)David Pflug
2024-01-14llama : check LLAMA_TRACE env for extra logging (#4929)Georgi Gerganov
* llama : minor fix indent * llama : check LLAMA_TRACE env for extra logging ggml-ci
2024-01-14llama : use LLAMA_LOG_ macros for loggingGeorgi Gerganov
2024-01-14Fix ffn_down quantization mix for MoE models (#4927)Kawrakow
* Fix ffn_down quantization mix for MoE models In #4872 I did not consider the part where every third tensor is quantized with more bits. Fir MoE this leads to tensors of the same layer being quantized with different number of bits, which is not considered as a possibility in the inference implementation (it is assumed all experts use the same quantization). * Fix the fix * Review suggestion --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-01-14llama : support WinXP build with MinGW 8.1.0 (#3419)Karthik Kumar Viswanathan
2024-01-142-bit quantizations (#4897)Kawrakow
* imatrix: load * imatrix: WIP * imatrix: Add Q2_K quantization * imatrix: also guard against Q2_K_S quantization without importance matrix * imatrix: guard even more against low-bit quantization misuse --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-01-14Make Q3_K_S be the same as olf Q3_K_L for Mixtral-8x7B (#4906)Kawrakow
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-01-13metal : remove old API (#4919)Georgi Gerganov
ggml-ci
2024-01-13llama : fix detokenization of non-special added-tokens (#4916)Georgi Gerganov
Co-authored-by: goerch <jhr.walter@t-online.de>
2024-01-13llama : minimize size used for state save/load (#4820)David Friehs
* examples : save-load-state: save only required state * llama : only reserve n_vocab * n_batch at most for logits llama_decode asserts that only n_batch tokens are passed each call, and n_ctx is expected to be bigger than n_batch. * llama : always reserve n_vocab * n_batch for logits llama_context de-serialization breaks if the contexts have differing capacity for logits and llama_decode will at maximum resize to n_vocab * n_batch. * llama : only save and restore used logits for batch sizes of 512 this reduces save state in the best case by around 62 MB, which can be a lot if planning to save on each message to allow regenerating messages. * llama : use ostringstream and istringstream for save and load * llama : serialize rng into minimum amount of space required * llama : break session version due to serialization changes
2024-01-13convert : update phi-2 to latest HF repo (#4903)Georgi Gerganov
* convert : update phi-2 to latest HF repo ggml-ci * py : try to fix flake stuff
2024-01-12llama : ggml-backend integration (#4766)slaren
* llama : ggml-backend integration * ggml-backend : add names to buffers * fix unmap after loading * batched-bench : add tensor_split param * llama : check for null tensor_split * ggml-backend : increase GGML_MAX_BACKENDS * improve graph splitting, partial fix for --no-kv-offload * cuda : add ggml-backend split buffer support * cuda : do not create buffer types for devices that don't exist (fixes usage without CUDA devices available) * ggml : fix null backend dereference (#4807) * ggml : fix null backend dereference * ggml : also check ggml_backend_is_cpu * test-backend-ops : check buffer allocation failures * llama : add cparam (split_mode) and command line argument (--split-mode, -sm) to configure the split mode (none, layer or row) * ggml : fix mul_mat_id work size * llama : rewrite session kv load/set without graphs * minor * llama : only initialize used backends, free backends on context free * llama : abort ctx if cuda backend init fails * llama : rewrite lora with ggml-backend and compute on CPU ggml-ci * llama : only map to a backend buffer the region of the file mapping containing the tensors used in the buffer * opencl : add ggml-backend buffer type * cuda : only use batched_cublas with batched mat muls (fixes fp16 tg perf) * llama : on Metal, by default offload the full model ggml-ci * metal : page align the data ptr (#4854) * Apply suggestions from code review Co-authored-by: Johannes Gäßler <johannesg@5d6.de> * cuda : fix split buffer free * address review comments * llama-bench : add split-mode parameter * fix whitespace * opencl : fix double initialization * server : add --split-mode parameter * use async copy and compute to improve multi-gpu performance ggml-ci * use async memcpys to copy the graph outputs to the CPU * fix opencl * use a host buffer for the cpu compute buffer for faster copies to the gpu --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
2024-01-12llama : remove redundant assert for StableLM (#4901)Georgi Gerganov
2024-01-12llama : fix typo "imp_embd" -> "inp_embd"Georgi Gerganov
2024-01-12llama : fix llm_build_k_shift to use correct n_rot (#4889)Georgi Gerganov
* llama : fix llm_build_k_shift to use correct n_rot ggml-ci * llama : always use hparams.n_rot for ggml_rope_custom ggml-ci * convert : fix persimmon conversion to write correct n_rot
2024-01-11llama : restore intended k-quants mixes for MoE models (#4872)Kawrakow
* 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>
2024-01-11ggml : SOTA 2-bit quants (add IQ2_XS) (#4856)Kawrakow
* 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>
2024-01-11main : print total token count and tokens consumed so far (#4874)pudepiedj
* Token count changes * Add show token count * Updating before PR * Two requested changes * Move param def posn
2024-01-10llama : add additional suffixes for model params (#4834)Brian
* llm_load_print_meta: Add additional suffixs for model params * Update llama.cpp model param log remove unneeded comments and convert from > to >=
2024-01-10llama : recognize 1B phi models (#4847)Austin
This update categorizes models with 24 layers as MODEL_1B, ensuring compatibility with different Phi model variants without impacting existing Phi-2 model functionality.
generated by cgit v1.2.3 (git 2.25.1) at 2025-10-05 08:51:57 +0000