metadata
base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
datasets:
- teknium/OpenHermes-2.5
language:
- en
tags:
- Llama-3
- instruct
- finetune
- chatml
- DPO
- RLHF
- gpt4
- synthetic data
- distillation
- function calling
- json mode
- axolotl
- llama-cpp
- gguf-my-repo
widget:
- example_title: Hermes 2 Pro
messages:
- role: system
content: >-
You are a sentient, superintelligent artificial general intelligence,
here to teach and assist me.
- role: user
content: >-
Write a short story about Goku discovering kirby has teamed up with
Majin Buu to destroy the world.
model-index:
- name: Hermes-2-Pro-Llama-3-8B
results: []
martintmv/Hermes-2-Pro-Llama-3-8B-Q4_K_S-GGUF
This model was converted to GGUF format from NousResearch/Hermes-2-Pro-Llama-3-8B using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on 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 martintmv/Hermes-2-Pro-Llama-3-8B-Q4_K_S-GGUF --hf-file hermes-2-pro-llama-3-8b-q4_k_s.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo martintmv/Hermes-2-Pro-Llama-3-8B-Q4_K_S-GGUF --hf-file hermes-2-pro-llama-3-8b-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 martintmv/Hermes-2-Pro-Llama-3-8B-Q4_K_S-GGUF --hf-file hermes-2-pro-llama-3-8b-q4_k_s.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo martintmv/Hermes-2-Pro-Llama-3-8B-Q4_K_S-GGUF --hf-file hermes-2-pro-llama-3-8b-q4_k_s.gguf -c 2048