The example-specific dependencies are required to be installed separately from their respective requirements.txt files if not using the ModelOpt docker image. Techniques Below is a short description of the tech
The speed and memory profiling are conducted using this script. We measured the average inference speed (tokens/s) and GPU memory usage of generating 2048 with the models in BF16, Int8, and Int4. Model Size Quantization Speed (Tokens/s) GPU Memory Usage 1.8B BF16 54.09 4.23GB Int8 ...
export QUANT_WEIGHT_PATH=/home/quant_weight # Single-chip quantization export ENABLE_QUANT=1 python3 generate_weights.py --model_path ${CHECKPOINT} python3 main.py --mode precision_dataset --model_path ${CHECKPOINT} --ceval_dataset ${DATASET} --batch 8 --device 0 # Dual-chip ...
In this post, we walk through an end-to-end example of fine-tuning the Llama2 large language model (LLM) using the QLoRA method. QLoRA combines the benefits of parameter efficient fine-tuning with 4-bit/8-bit quantization to further reduce the resources required...
这里作者加入一个Quantization Dropout的trick，就是在训练时随机dropout掉一些Quantization层（以便各个层都...
Examples of illumination normalization are shown onFigure 4.5. The images of the first row, illuminated from different directions, are fitted. Renderings of the fitting results are shown in the second row. The same renderings, but using the illumination parameters from the leftmost input image, ...
3. Quantization: Quantization reduces the precision of weights and activations from float32 to lower bit widths like int8 or int4. This shrinks model size and speeds up computation on integer-optimized hardware. Quantization applies techniques like clipping, rounding, and rescaling to ...
one promising research direction is themodel compressiontechnique. For example, knowledge distillation is commonly used to transform large and powerful models into simpler models with a minor decrease in accuracy [64]. Additionally, one can use quantization, weight sharing, and careful coding of networ...
2.0 × 10−3s. In contrast, the calculation time for the simulated annealing and the exact solution is 3.74 × 10−1s and 8.61 × 102s, respectively. The proposed method using the quantum annealing enables higher-speed quantization than the brute-force search and higher performance than the...
Plain C/C++ implementation without any dependencies Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks AVX, AVX2, AVX512 and AMX support for x86 architectures 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for ...