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AXONVERTEX-AI-RESEARCH/InfLLM-V2-Short-Dense-Base-Q8_0-GGUF
InfLLM-V2-Short-Dense-Base-Q8_0-GGUF is a machine learning model from AXONVERTEX-AI-RESEARCH. 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 apache-2.0.
This model was converted to GGUF format from openbmb/InfLLM-V2-Short-Dense-Base using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.
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From the Hugging Face model README
This model was converted to GGUF format from openbmb/InfLLM-V2-Short-Dense-Base 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 AXONVERTEX-AI-RESEARCH/InfLLM-V2-Short-Dense-Base-Q8_0-GGUF --hf-file infllm-v2-short-dense-base-q8_0.gguf -p "The meaning to life and the universe is"
llama-server --hf-repo AXONVERTEX-AI-RESEARCH/InfLLM-V2-Short-Dense-Base-Q8_0-GGUF --hf-file infllm-v2-short-dense-base-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 AXONVERTEX-AI-RESEARCH/InfLLM-V2-Short-Dense-Base-Q8_0-GGUF --hf-file infllm-v2-short-dense-base-q8_0.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo AXONVERTEX-AI-RESEARCH/InfLLM-V2-Short-Dense-Base-Q8_0-GGUF --hf-file infllm-v2-short-dense-base-q8_0.gguf -c 2048