Comments (1)
Looks like all of the AutoQuant, Autogguf, etc notebooks on colab are broken too :(
from llama.cpp.
Related Issues (20)
- Bug: infill reference crashed HOT 6
- Bug: Cannot quantize a model to BF16 due to an overflow in gguf/quants.py HOT 5
- Bug: llama-cli templating does buf.resize(-1) if the model's template is not supported, causing crash HOT 2
- Request: Add support for Qwen2 Embedding model: Alibaba-NLP/gte-Qwen2-7B-instructFeature
- Bug: error loading model: llama_model_loader: failed to load model HOT 1
- Bug: `cmake -B build -DLLAMA_CUDA=ON -DLLAMA_NATIVE=ON` gives "deprecated" warning but then compiled CPU-only verrsion HOT 4
- Bug: llama.cpp binaries are compiled dynamically and the library is missing! HOT 4
- Bug: on AMD gpu, it offloads all the work to the CPU unless you specify --n-gpu-layers on the llama-cli command line HOT 18
- Bug: GGML can no longer be statically linked to llama.cpp due to the source code reorganization HOT 7
- Feature Request: Why is there no pre-compiled Windows version of AMD ROCm? HOT 2
- Feature Request: Why is there no pre-compiled Windows version of AMD ROCm? HOT 1
- Bug: Cannot load DeepSeek-Coder-V2-Instruct HOT 11
- Bug: llama-infill segmentation fault if missing --in-suffix
- Bug: quantized gemma 27b output still wrong after tokenizer fix HOT 10
- LLava/ MobileVLM on android platform
- Bug: The inference speed of building with HIPBLAS (gfx1100) is very slow, only 2~5t/s HOT 6
- Feature Request: Installable package via winget HOT 1
- I have found a solution to the problem myself HOT 3
- @ggerganov!!! Why can't llama.cpp be used on the server side to provide one-to-many services? HOT 1
- Bug: Gemma-2 not supported on b3262 HOT 5
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from llama.cpp.