GPTQ is a post-training quantization technique, making it an ideal choice for very large models where full training or even fine-tuning can be prohibitively expensive. It has the capability to quantize models to 2-, 3-, or 4-bit format, offering flexibility based on your specific needs. GP...
it seems the run_llama2_sq.py L234: quantization.fit() do smooth_quant and quantize sequentially, can i get smoothed model(FP32) to save before do quantization? if not, can i get best smooth_quant alpha(sq_alpha) by AutoTuneStrategy()._transfer_alpha(), then , reproduce correspondingly...
For all responses that an LLM generates, it typically uses a probability distribution to determine what token it is going to provide next. In situations where it has a strong knowledge base of a certain subject, these probabilities for the next word/token can be 99% or higher. But in ...
Currently we are trying to run inference with pretrained BLOOM model. However, the loading takes very long due to DeepSpeed sharding in runtime. Since there is a pre-sharded version of BLOOM: microsoft/bloom-deepspeed-inference-fp16 Is t...
and run inference either on a local RTX system, a cloud endpoint hosted onNVIDIA’s API catalogor usingNVIDIA NIMmicroservices. The project can be adapted to use various models, endpoints and containers, and provides the ability for developers to quantize models to run on their GPU of choice....
DiskANN gives users the option to quantize vectors within the index. This process reduces the vector size, consequently shrinking the index size significantly and expediting searches. While this might entail a marginal decline in query accuracy, PostgreSQL already stores the full-scale vectors in the...
{OUTTYPE}.gguf as production# Please REPLACE $LLAMA_MODEL_LOCATION with your model locationpython3 convert.py$LLAMA_MODEL_LOCATION# Compile the model in specified outtypepython3 convert$LLAMA_MODEL_LOCATION--outtype q8_0# quantize the modelquantize ./models/7B/ggml-model-f16.gguf ./models/...
For all responses that an LLM generates, it typically uses a probability distribution to determine what token it is going to provide next. In situations where it has a strong knowledge base of a certain subject, these probabilities for the next word/token can be 99% or higher. But in ...
Hello! I'm trying to run Vicuna InstructBLIP, but sadly, I can't make it work. I installed LAVIS directly from your repo following the step 3 of the installation guide, and I'm using the following code: import torch from lavis.models imp...
If you run out of GPU memory, pass the parameter--quantizeto the script. python test_llama_squad.py --adapter_name=results/final_checkpoints This generates a CSV fileresults/results.csvwhich you can summarize with python summarize_results.py ...