mx003/viper-llama31-8b-Q4_K_M-GGUF overview
mx003/viper llama31 8b Q4 K M GGUF This model was converted to GGUF format from mx003/viper llama31 8b https://huggingface.co/mx003/viper llama31 8b 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 |
|---|---|---|---|---|
| viper-llama31-8b-q4_k_m.gguf | GGUF | Q4_K_M | 4.58 GB | Download |
Model Details
Model README
---
license: llama3.1
base_model: mx003/viper-llama31-8b
datasets:
- mx003/viper
tags:
- lora
- cybersecurity
- cve
- viper
- information-extraction
- llama-cpp
- gguf-my-repo
---
mx003/viper-llama31-8b-Q4_K_M-GGUF
This model was converted to GGUF format from mx003/viper-llama31-8b 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/viper-llama31-8b-Q4_K_M-GGUF --hf-file viper-llama31-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo mx003/viper-llama31-8b-Q4_K_M-GGUF --hf-file viper-llama31-8b-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/viper-llama31-8b-Q4_K_M-GGUF --hf-file viper-llama31-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo mx003/viper-llama31-8b-Q4_K_M-GGUF --hf-file viper-llama31-8b-q4_k_m.gguf -c 2048Run mx003/viper-llama31-8b-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