Triangle104/Qwen2.5-Gutenberg-Doppel-14B-Q4_K_S-GGUF
This model was converted to GGUF format from nbeerbower/Qwen2.5-Gutenberg-Doppel-14B
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:
Qwen/Qwen2.5-14B-Instruct finetuned on jondurbin/gutenberg-dpo-v0.1 and nbeerbower/gutenberg2-dpo.
Method ORPO tuned with 4x A40 for 3 epochs.
Thank you @ParasiticRogue for sponsoring.
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/Qwen2.5-Gutenberg-Doppel-14B-Q4_K_S-GGUF --hf-file qwen2.5-gutenberg-doppel-14b-q4_k_s.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Triangle104/Qwen2.5-Gutenberg-Doppel-14B-Q4_K_S-GGUF --hf-file qwen2.5-gutenberg-doppel-14b-q4_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/Qwen2.5-Gutenberg-Doppel-14B-Q4_K_S-GGUF --hf-file qwen2.5-gutenberg-doppel-14b-q4_k_s.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Triangle104/Qwen2.5-Gutenberg-Doppel-14B-Q4_K_S-GGUF --hf-file qwen2.5-gutenberg-doppel-14b-q4_k_s.gguf -c 2048
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Model tree for Triangle104/Qwen2.5-Gutenberg-Doppel-14B-Q4_K_S-GGUF
Base model
Qwen/Qwen2.5-14BCollections including Triangle104/Qwen2.5-Gutenberg-Doppel-14B-Q4_K_S-GGUF
Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard80.910
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard48.240
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard0.000
- acc_norm on GPQA (0-shot)Open LLM Leaderboard11.070
- acc_norm on MuSR (0-shot)Open LLM Leaderboard10.020
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard43.570