Joe57005/Marco-Nano-Instruct-Q4_0-GGUF overview
Joe57005/Marco Nano Instruct Q4 0 GGUF This model was converted to GGUF format from ATH MaaS/Marco Nano Instruct https://huggingface.co/ATH MaaS/Marco Nano Ins…
Runs locally from ~4.26 GB disk (8 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| marco-nano-instruct-q4_0.gguf | GGUF | Q4_0 | 4.26 GB | Download |
Model Details
Model README
---
license: apache-2.0
language:
- en
- zh
- ar
- de
- es
- fr
- ko
- ja
- pt
- tr
- id
- it
- nl
- pl
- ru
- vi
- th
- he
- uk
- ms
- bn
- cs
- ur
- kk
- el
- ro
- hu
- ne
- az
library_name: transformers
tags:
- moe
- mixture-of-experts
- multilingual
- upcycling
- llama-cpp
- gguf-my-repo
datasets:
- allenai/Dolci-Instruct-SFT
- nvidia/Nemotron-Cascade-2-SFT-Data
- nvidia/Nemotron-RL-instruction_following
- nvidia/Nemotron-RL-instruction_following-structured_outputs
- nvidia/Nemotron-RL-ReasoningGym-v1
- nvidia/Nemotron-RL-knowledge-mcqa
- nvidia/Nemotron-Cascade-RL-RLHF
- BytedTsinghua-SIA/DAPO-Math-17k
- Skywork/Skywork-OR1-RL-Data
- nvidia/Nemotron-SFT-Multilingual-v1
base_model: ATH-MaaS/Marco-Nano-Instruct
---
Joe57005/Marco-Nano-Instruct-Q4_0-GGUF
This model was converted to GGUF format from ATH-MaaS/Marco-Nano-Instruct using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo Joe57005/Marco-Nano-Instruct-Q4_0-GGUF --hf-file marco-nano-instruct-q4_0.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Joe57005/Marco-Nano-Instruct-Q4_0-GGUF --hf-file marco-nano-instruct-q4_0.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo Joe57005/Marco-Nano-Instruct-Q4_0-GGUF --hf-file marco-nano-instruct-q4_0.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Joe57005/Marco-Nano-Instruct-Q4_0-GGUF --hf-file marco-nano-instruct-q4_0.gguf -c 2048Run Joe57005/Marco-Nano-Instruct-Q4_0-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models