Comments (5)
Ah you need the pytorch nightly build for this repo.
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One possible reason for encountering this issue could be the use of a CPU build. Could you check torch.cuda.is_available()
just to be sure?
from gpt-fast.
Thanks for the response:
torch.cuda.is_available() = True
No problems training either
Also note I'm using this docker image:
nvcr.io/nvidia/pytorch:23.10-py3
from gpt-fast.
Have you solved this problem? I got same problem when I quantize llama 7b int4.
from gpt-fast.
you should use the nightly version of torch or at least the recent 2.2 branch cut, its a newish op that was added for int4 support.
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Related Issues (20)
- Speculative decoding with draft model:TinyLlama-1.1B
- Can't quantize to int4 and can't compile on RTX2080Ti HOT 2
- Int4 perplexity
- index out of range: No transformer config could be loaded HOT 1
- Reducing Latency in Application with Torch Compilation: Initialization and Inference Optimization
- int4/int4-gptq support in Mixtral 8x7B HOT 2
- CUDA error if enabling compile_prefill for quantization model (int8) HOT 7
- Question about large sequence length attention kernels HOT 1
- int8 Woq raise Codegen Error with `--compile_prefill` HOT 4
- On the memory usage of `ConditionalFeedForward` HOT 4
- Tiny Llamas Not Found HOT 2
- INT4 quantization not working on MI210 HOT 2
- Input token length question HOT 2
- Naming: n_local_heads -> n_kv_heads
- mmap issue in bf16 of gpt-fast
- Tensor Parallel Inside notebook
- Missing Keys in state_dict HOT 1
- Throughput Benchmark Scripts HOT 2
- Hard-coded Llama-3 model name pattern matching breaks scripts/convert_hf_checkpoint.py
- `meta-llama/Meta-Llama-3-8B-Instruct` generates gibberish for long prompts HOT 5
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