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hellork/SmolLM-1.7B-Instruct-Q5_K_M-GGUF
SmolLM-1.7B-Instruct-Q5_K_M-GGUF is a machine learning model from hellork. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
This model was converted to GGUF format from HuggingFaceTB/SmolLM-1.7B-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.
Downloads · 30 days
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.gguf1.2 GB · 100%
From the Hugging Face model README
This model was converted to GGUF format from HuggingFaceTB/SmolLM-1.7B-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.
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
llama-cli --hf-repo hellork/SmolLM-1.7B-Instruct-Q5_K_M-GGUF --hf-file smollm-1.7b-instruct-q5_k_m-imat.gguf -p "The meaning to life and the universe is"
llama-server --hf-repo hellork/SmolLM-1.7B-Instruct-Q5_K_M-GGUF --hf-file smollm-1.7b-instruct-q5_k_m-imat.gguf -c 2048
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.
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.
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/SmolLM-1.7B-Instruct-Q5_K_M-GGUF --hf-file smollm-1.7b-instruct-q5_k_m-imat.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo hellork/SmolLM-1.7B-Instruct-Q5_K_M-GGUF --hf-file smollm-1.7b-instruct-q5_k_m-imat.gguf -c 2048