mx003/cve_Vector_model-Q4_K_M-GGUF overview
mx003/cve Vector model Q4 K M GGUF This model was converted to GGUF format from mx003/cve Vector model https://huggingface.co/mx003/cve Vector model using llam…
Runs locally from ~4.58 GB disk (8 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| cve_vector_model-q4_k_m.gguf | GGUF | Q4_K_M | 4.58 GB | Download |
Model Details
Model README
---
base_model: mx003/cve_Vector_model
tags:
- llama-cpp
- gguf-my-repo
---
mx003/cve_Vector_model-Q4_K_M-GGUF
This model was converted to GGUF format from mx003/cve_Vector_model 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 mx003/cve_Vector_model-Q4_K_M-GGUF --hf-file cve_vector_model-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo mx003/cve_Vector_model-Q4_K_M-GGUF --hf-file cve_vector_model-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 mx003/cve_Vector_model-Q4_K_M-GGUF --hf-file cve_vector_model-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo mx003/cve_Vector_model-Q4_K_M-GGUF --hf-file cve_vector_model-q4_k_m.gguf -c 2048Run mx003/cve_Vector_model-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