Experts for GPU-Poors
Collection
GGUFs, conventional and k-quants – both without imatrix. This should be faster for CPU inference. Right now DeepSee MoEs (Mixture of Experts)
•
5 items
•
Updated
This model was converted to GGUF format from deepseek-ai/DeepSeek-Coder-V2-Lite-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 phi0112358/DeepSeek-Coder-V2-Lite-Instruct-Q8_0-GGUF --hf-file deepseek-coder-v2-lite-instruct-q8_0.gguf -p "The meaning to life and the universe is"
llama-server --hf-repo phi0112358/DeepSeek-Coder-V2-Lite-Instruct-Q8_0-GGUF --hf-file deepseek-coder-v2-lite-instruct-q8_0.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 phi0112358/DeepSeek-Coder-V2-Lite-Instruct-Q8_0-GGUF --hf-file deepseek-coder-v2-lite-instruct-q8_0.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo phi0112358/DeepSeek-Coder-V2-Lite-Instruct-Q8_0-GGUF --hf-file deepseek-coder-v2-lite-instruct-q8_0.gguf -c 2048
Base model
deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct