This model was converted to GGUF format from THUDM/chatglm3-6b 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)

bash

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo hellork/chatglm3-6b-IQ4_NL-GGUF --hf-file chatglm3-6b-iq4_nl-imat.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo hellork/chatglm3-6b-IQ4_NL-GGUF --hf-file chatglm3-6b-iq4_nl-imat.gguf -c 2048

The Ship's Computer:

whisper_dictation

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.

bash

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
cd whisper_dictation
./whisper_cpp_client.py

See the docs for tips on integrating with llama.cpp server, enabling the computer to talk back, draw AI images, carry out voice commands, and other features.

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/chatglm3-6b-IQ4_NL-GGUF --hf-file chatglm3-6b-iq4_nl-imat.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo hellork/chatglm3-6b-IQ4_NL-GGUF --hf-file chatglm3-6b-iq4_nl-imat.gguf -c 2048