metadata

base_model: vicgalle/Configurable-Llama-3.1-8B-Instruct
datasets:
  - vicgalle/configurable-system-prompt-multitask
library_name: transformers
license: apache-2.0
tags:
  - safety
  - uncensored
  - llama-3.1
  - llama-cpp
  - gguf-my-repo

hellork/Configurable-Llama-3.1-8B-Instruct-IQ4_NL-GGUF

This model was converted to GGUF format from vicgalle/Configurable-Llama-3.1-8B-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 hellork/Configurable-Llama-3.1-8B-Instruct-IQ4_NL-GGUF --hf-file configurable-llama-3.1-8b-instruct-iq4_nl-imat.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo hellork/Configurable-Llama-3.1-8B-Instruct-IQ4_NL-GGUF --hf-file configurable-llama-3.1-8b-instruct-iq4_nl-imat.gguf -c 2048

The Ship's Computer:

Interact with this model by speaking to it. Lean, fast, & private, networked speech to text, AI images, multi-modal voice chat, control apps, webcam, and sound with less than 4GiB of VRAM. whisper_dictation

Quick start

git clone -b main --single-branch https://github.com/themanyone/whisper_dictation.git
pip install -r whisper_dictation/requirements.txt

git clone https://github.com/ggerganov/whisper.cpp
cd whisper.cpp
GGML_CUDA=1 make -j # assuming CUDA is available. see docs
ln -s server ~/.local/bin/whisper_cpp_server # (just put it somewhere in $PATH)
whisper_cpp_server -l en -m models/ggml-tiny.en.bin --port 7777

# -ngl option assumes CUDA or othr AI acceleration is available. see docs
llama-server --hf-repo hellork/calme-2.1-qwen2-7b-IQ4_NL-GGUF --hf-file configurable-llama-3.1-8b-instruct-iq4_nl-imat.gguf -c 2048 -ngl 17 --port 8888

cd whisper_dictation
./whisper_cpp_client.py

Install llama.cpp via git:

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 hellork/Configurable-Llama-3.1-8B-Instruct-IQ4_NL-GGUF --hf-file configurable-llama-3.1-8b-instruct-iq4_nl-imat.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo hellork/Configurable-Llama-3.1-8B-Instruct-IQ4_NL-GGUF --hf-file configurable-llama-3.1-8b-instruct-iq4_nl-imat.gguf -c 2048