Llama_model_load: n_vocab = 32000 llama_model_load: n_ctx = 512 llama_model_load: n_embd = 4096 llama_model_load: n_mult = 256 llama_model_load: n_head = 32 llama_model_load: n_layer = 32 llama_model_load: n_rot = 128 llama_model_load: f16 = 2 llama_model_load: n_ff = 11008 llama_model_load: ggml ctx size = 4529.34 MB llama_model_load: memory_size = 512.00 MB, n_mem = 16384 llama_model_load. Main: seed = 1678486056 llama_model_load: loading model from './models/7B/ggml-model-q4_0.bin' - please wait. I./ examples - O3 - DNDEBUG - std= c++ 11 - fPIC - pthread I LDFLAGS: - framework Accelerate I CC: Apple clang version 14.0. O3 - DNDEBUG - std= c11 - fPIC - pthread - DGGML_USE_ACCELERATE I CXXFLAGS: - I. I UNAME_S: Darwin I UNAME_P: arm I UNAME_M: arm64 I CFLAGS: - I. bin - p "Building a website can be done in 10 simple steps:" - n 512 I llama. Baichuan-7B and its derivations (such as baichuan-7b-sft).Chinese LLaMA / Alpaca and Chinese LLaMA-2 / Alpaca-2.This project is for educational purposes and servesĪs the main playground for developing new features for the ggml library. Since then, the project has improved significantly thanks to many contributions. The original implementation of llama.cpp was hacked in an evening. Supports OpenBLAS/Apple BLAS/ARM Performance Lib/ATLAS/BLIS/Intel MKL/NVHPC/ACML/SCSL/SGIMATH and more in BLAS.4-bit, 5-bit and 8-bit integer quantization support.AVX, AVX2 and AVX512 support for x86 architectures.Apple silicon first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks.Plain C/C++ implementation without dependencies.The main goal of llama.cpp is to run the LLaMA model using 4-bit integer quantization on a MacBook Seminal papers and background on the models.Obtaining the Facebook LLaMA original model and Stanford Alpaca model data.p1 : LLM-based code completion engine at the edge : ggml-org/p1#1.k-quants now support super-block size of 64: #2001.
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