Dalia90/ur_llama_merge-Q8_0-GGUF overview
Dalia90/ur llama merge Q8 0 GGUF This model was converted to GGUF format from Dalia90/ur llama merge https://huggingface.co/Dalia90/ur llama merge using llama.…
Runs locally from ~7.95 GB disk (8 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| ur_llama_merge-q8_0.gguf | GGUF | Q8_0 | 7.95 GB | Download |
Model Details
Model README
---
base_model: Dalia90/ur_llama_merge
tags:
- llama-cpp
- gguf-my-repo
---
Dalia90/ur_llama_merge-Q8_0-GGUF
This model was converted to GGUF format from Dalia90/ur_llama_merge 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 Dalia90/ur_llama_merge-Q8_0-GGUF --hf-file ur_llama_merge-q8_0.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Dalia90/ur_llama_merge-Q8_0-GGUF --hf-file ur_llama_merge-q8_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 Dalia90/ur_llama_merge-Q8_0-GGUF --hf-file ur_llama_merge-q8_0.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Dalia90/ur_llama_merge-Q8_0-GGUF --hf-file ur_llama_merge-q8_0.gguf -c 2048Run Dalia90/ur_llama_merge-Q8_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