metadata

license: apache-2.0
library_name: transformers
base_model: nbeerbower/EVA-Gutenberg3-Qwen2.5-32B
datasets:
  - jondurbin/gutenberg-dpo-v0.1
  - nbeerbower/gutenberg2-dpo
  - nbeerbower/gutenberg-moderne-dpo
tags:
  - llama-cpp
  - gguf-my-repo

Triangle104/EVA-Gutenberg3-Qwen2.5-32B-Q5_K_M-GGUF

This model was converted to GGUF format from nbeerbower/EVA-Gutenberg3-Qwen2.5-32B 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:

EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2 finetuned on jondurbin/gutenberg-dpo-v0.1, nbeerbower/gutenberg2-dpo, and nbeerbower/gutenberg-moderne-dpo.

Method

ORPO tuned with 8x A100 for 2 epochs.

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/EVA-Gutenberg3-Qwen2.5-32B-Q5_K_M-GGUF --hf-file eva-gutenberg3-qwen2.5-32b-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/EVA-Gutenberg3-Qwen2.5-32B-Q5_K_M-GGUF --hf-file eva-gutenberg3-qwen2.5-32b-q5_k_m.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/EVA-Gutenberg3-Qwen2.5-32B-Q5_K_M-GGUF --hf-file eva-gutenberg3-qwen2.5-32b-q5_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Triangle104/EVA-Gutenberg3-Qwen2.5-32B-Q5_K_M-GGUF --hf-file eva-gutenberg3-qwen2.5-32b-q5_k_m.gguf -c 2048