Comments (1)
@VityaVitalich actually it is an artifact from GPTQ code. tar
is never used in our code (as well as in the original repository).
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Related Issues (20)
- Case Study: Instruction Tuning on AQLM Models HOT 7
- ERROR: Could not find a version that satisfies the requirement aqlm[gpu]==1.0.0 HOT 2
- AttributeError: module 'torch.library' has no attribute 'impl_abstract' HOT 4
- Are there any tools that can serve AQLM quantized models? HOT 4
- Supported Models HOT 2
- How model_seqlen affects quantization quality HOT 5
- Reproduce perplexity HOT 6
- RuntimeError: Error building extension 'codebook_cuda': [1/3] /usr/local/cuda/bin/nvcc --generate-dependencies-with-compile --dependency-output cuda_kernel.cuda.o.d HOT 2
- How to fine-tune the compressed model HOT 3
- optimized 1x16 and 2x8 decompression/dequantization kernels now exist HOT 8
- 33B llama quantization post-inference time HOT 3
- Please help quantize sayhan/Qwen1.5-72B-Chat-LLaMAfied HOT 2
- print Total number of parameters is incomplete. HOT 3
- Aqlm models cannot be trained in parallel. HOT 5
- Question: How to choose a dataset for quantizing with AQLM a model like Mistral 7b-Instruct v0.2 HOT 9
- aqlm/inference_kernels/cuda_kernel.cu compilation errors HOT 10
- DBRX Support HOT 6
- Any lightweight OpenAI API compatible HTTP server that can be used to serve AQLM models yet? HOT 1
- AQLM support for Cohere Command-R + HOT 3
- Request: AQLM quantization of the new Mixtral 8x22B HOT 3
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