Text Generation
memra
GGUF
nvfp4
speculative-decoding
mtp
conversational
blackwell
qwen3
Eval Results (legacy)
Instructions to use tiyuvta/Qwen3.8-27B-NVFP4-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- memra
How to use tiyuvta/Qwen3.8-27B-NVFP4-MTP-GGUF 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/Qwen3.8-27B-NVFP4-MTP-GGUF:Q4_K_M MEMRA_CHAT=1 run-gen hf:tiyuvta/Qwen3.8-27B-NVFP4-MTP-GGUF --prompt "Explain KV caches in one sentence."
# Or an OpenAI-compatible server on 127.0.0.1:8080. MEMRA_MODELS="model=hf:tiyuvta/Qwen3.8-27B-NVFP4-MTP-GGUF" memra-server
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba0cdcd0ced7560d890e4192c43846f656eec053f8f0f5677a55cc50ac15a9bd
- Size of remote file:
- 1.24 GB
- SHA256:
- a47848be6f918b93f9647b67b302382c750774e83907d3cc7b6f2c42057b8e07
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