metadata

base_model: arcee-ai/Virtuoso-Medium-v2
library_name: transformers
tags:
  - mergekit
  - merge
  - llama-cpp
  - gguf-my-repo
license: apache-2.0

Triangle104/Virtuoso-Medium-v2-Q3_K_L-GGUF

This model was converted to GGUF format from arcee-ai/Virtuoso-Medium-v2 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-Medium-v2 (32B) is our next-generation, 32-billion-parameter language model that builds upon the original Virtuoso-Medium architecture. This version is distilled from Deepseek-v3, leveraging an expanded dataset of 5B+ tokens worth of logits. It achieves higher benchmark scores than our previous release (including surpassing Arcee-Nova 2024 in certain tasks).

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-Medium-v2-Q3_K_L-GGUF --hf-file virtuoso-medium-v2-q3_k_l.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Virtuoso-Medium-v2-Q3_K_L-GGUF --hf-file virtuoso-medium-v2-q3_k_l.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-Medium-v2-Q3_K_L-GGUF --hf-file virtuoso-medium-v2-q3_k_l.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Triangle104/Virtuoso-Medium-v2-Q3_K_L-GGUF --hf-file virtuoso-medium-v2-q3_k_l.gguf -c 2048