GPTQ vs EXL2 vs AWQ vs Q4_K_M model sizes : r/Oobabooga #304
Labels
llm
Large Language Models
llm-benchmarks
testing and benchmarking large language models
llm-experiments
experiments with large language models
llm-inference-engines
Software to run inference on large language models
llm-quantization
All about Quantized LLM models and serving
GPTQ vs EXL2 vs AWQ vs Q4_K_M model sizes
Mod Post
Size (mb) Model
16560 Phind_Phind-CodeLlama-34B-v2-EXL2-4.000b
17053 Phind_Phind-CodeLlama-34B-v2-EXL2-4.125b
17463 Phind-CodeLlama-34B-v2-AWQ-4bit-128g
17480 Phind-CodeLlama-34B-v2-GPTQ-4bit-128g-actorder
17548 Phind_Phind-CodeLlama-34B-v2-EXL2-4.250b
18143 Phind_Phind-CodeLlama-34B-v2-EXL2-4.400b
19133 Phind_Phind-CodeLlama-34B-v2-EXL2-4.650b
19284 phind-codellama-34b-v2.Q4_K_M.gguf
19320 Phind-CodeLlama-34B-v2-AWQ-4bit-32g
19337 Phind-CodeLlama-34B-v2-GPTQ-4bit-32g-actorder
I created all these EXL2 quants to compare them to GPTQ and AWQ. The preliminary result is that EXL2 4.4b seems to outperform GPTQ-4bit-32g while EXL2 4.125b seems to outperform GPTQ-4bit-128g while using less VRAM in both cases.
I couldn't test AWQ yet because my quantization ended up broken, possibly due to this particular model using NTK scaling, so I'll probably have to go through the fun of burning my GPU for 16 hours again to quantize and evaluate another model so that a conclusion can be reached.
Also no idea if Phind-CodeLlama is actually good. WizardCoder-Python might be better.
Suggested labels
"LLM-Quantization"
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