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hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF
deepseek-coder-6.7b-instruct-IQ4_NL-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. The card lists the license as other.
This model was converted to GGUF format from deepseek-ai/deepseek-coder-6.7b-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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.gguf3.8 GB · 100%
From the Hugging Face model README
This model was converted to GGUF format from deepseek-ai/deepseek-coder-6.7b-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/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF --hf-file deepseek-coder-6.7b-instruct-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
llama-server --hf-repo hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF --hf-file deepseek-coder-6.7b-instruct-iq4_nl-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. 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 calme-2.1-qwen2-7b-iq4_nl-imat.gguf -c 2048 -ngl 17 --port 8888
cd whisper_dictation
./whisper_cpp_client.py
Step 1: 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/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF --hf-file deepseek-coder-6.7b-instruct-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo hellork/deepseek-coder-6.7b-instruct-IQ4_NL-GGUF --hf-file deepseek-coder-6.7b-instruct-iq4_nl-imat.gguf -c 2048