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tiyuvta
/
GLM-5.3-Flash-NVFP4

Text Generation
memra
Safetensors
English
Chinese
glm5_next
nvfp4
fp4
4-bit precision
modelopt
w4a16
quantized
Mixture of Experts
blackwell
conversational
tool-calling
8-bit precision
Model card Files Files and versions
xet
Community
4

Instructions to use tiyuvta/GLM-5.3-Flash-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • memra

    How to use tiyuvta/GLM-5.3-Flash-NVFP4 with memra:

    # memra serves NVIDIA Blackwell workstation and consumer cards (sm_120a), with a
    # compile-gated Hopper lane. Prebuilt binaries need Linux x86_64 and driver 580+,
    # and no CUDA toolkit.
    curl -fsSL https://raw.githubusercontent.com/avifenesh/memra/main/tools/install.sh | sh
    # One chat-templated generation. In a repo with several GGUF files, append
    # :<substring> to choose one, for example hf:tiyuvta/GLM-5.3-Flash-NVFP4:Q4_K_M
    MEMRA_CHAT=1 run-gen hf:tiyuvta/GLM-5.3-Flash-NVFP4 --prompt "Explain KV caches in one sentence."
    # Or an OpenAI-compatible server on 127.0.0.1:8080.
    MEMRA_MODELS="model=hf:tiyuvta/GLM-5.3-Flash-NVFP4" memra-server
  • Notebooks
  • Google Colab
  • Kaggle
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Context section correction

#3 opened 1 day ago by
Avifenesh

Context section correction

#2 opened 1 day ago by
Avifenesh

Context section: 1,035,357 tokens demonstrated (1m-demo receipts); the monolithic-prefill wall is fixed by the chunk schedule; honest 4-card/latency boundaries stated

#1 opened 1 day ago by
Avifenesh
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