library_name: transformers
license: apache-2.0
base_model: nbeerbower/Mistral-Nemo-Prism-12B-v7
datasets:
- nbeerbower/Arkhaios-DPO
- nbeerbower/Purpura-DPO
tags:
- llama-cpp
- gguf-my-repo
Triangle104/Mistral-Nemo-Prism-12B-v7-Q6_K-GGUF
This model was converted to GGUF format from nbeerbower/Mistral-Nemo-Prism-12B-v7 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:
EXPERIMENTAL MODEL!!!
Mahou-1.5-mistral-nemo-12B-lorablated finetuned on Arkhaios-DPO and Purpura-DPO.
The goal was to reduce archaic language and purple prose in a completely uncensored model. Method
ORPO tuned with 8x A40 for 10 epochs.
For this version, beta was increased to 2.
In conclusion, LoRA does not seem to be able to completely remove some of the language issues deeply embedded in the model.
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/Mistral-Nemo-Prism-12B-v7-Q6_K-GGUF --hf-file mistral-nemo-prism-12b-v7-q6_k.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Triangle104/Mistral-Nemo-Prism-12B-v7-Q6_K-GGUF --hf-file mistral-nemo-prism-12b-v7-q6_k.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/Mistral-Nemo-Prism-12B-v7-Q6_K-GGUF --hf-file mistral-nemo-prism-12b-v7-q6_k.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Triangle104/Mistral-Nemo-Prism-12B-v7-Q6_K-GGUF --hf-file mistral-nemo-prism-12b-v7-q6_k.gguf -c 2048