metadata

base_model: arcee-ai/Virtuoso-Lite
library_name: transformers
license: other
tags:
  - mergekit
  - merge
  - llama-cpp
  - gguf-my-repo

Triangle104/Virtuoso-Lite-Q5_K_S-GGUF

This model was converted to GGUF format from arcee-ai/Virtuoso-Lite using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Model details:

Virtuoso-Lite (10B) is our next-generation, 10-billion-parameter language model based on the Llama-3 architecture. It is distilled from Deepseek-v3 using ~1.1B tokens/logits, allowing it to achieve robust performance at a significantly reduced parameter count compared to larger models. Despite its compact size, Virtuoso-Lite excels in a variety of tasks, demonstrating advanced reasoning, code generation, and mathematical problem-solving capabilities.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Virtuoso-Lite-Q5_K_S-GGUF --hf-file virtuoso-lite-q5_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Virtuoso-Lite-Q5_K_S-GGUF --hf-file virtuoso-lite-q5_k_s.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 Triangle104/Virtuoso-Lite-Q5_K_S-GGUF --hf-file virtuoso-lite-q5_k_s.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Triangle104/Virtuoso-Lite-Q5_K_S-GGUF --hf-file virtuoso-lite-q5_k_s.gguf -c 2048