Darlanio/MicroLLM-005-Q4_K_M-GGUF overview
Darlanio/MicroLLM 005 Q4 K M GGUF This model was converted to GGUF format from Darlanio/MicroLLM 005 https://huggingface.co/Darlanio/MicroLLM 005 using llama.c…
Runs locally from ~51.3 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| microllm-005-q4_k_m.gguf | GGUF | Q4_K_M | 51.3 MB | Download |
Model Details
Model README
---
license: gpl-3.0
tags:
- llama-cpp
- gguf-my-repo
base_model: Darlanio/MicroLLM-005
---
Darlanio/MicroLLM-005-Q4_K_M-GGUF
This model was converted to GGUF format from Darlanio/MicroLLM-005 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 Darlanio/MicroLLM-005-Q4_K_M-GGUF --hf-file microllm-005-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Darlanio/MicroLLM-005-Q4_K_M-GGUF --hf-file microllm-005-q4_k_m.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 Darlanio/MicroLLM-005-Q4_K_M-GGUF --hf-file microllm-005-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Darlanio/MicroLLM-005-Q4_K_M-GGUF --hf-file microllm-005-q4_k_m.gguf -c 2048Run Darlanio/MicroLLM-005-Q4_K_M-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models