base_model: Spestly/Athena-3-3B
language:
- en
- zh
- fr
- es
- pt
- de
- it
- ru
- ja
- ko
- vi
- th
- ar
- fa
- he
- tr
- cs
- pl
- hi
- bn
- ur
- id
- ms
- lo
- my
- ceb
- km
- tl
- nl
library_name: transformers
license: mit
tags:
- chemistry
- biology
- code
- text-generation-inference
- STEM
- unsloth
- llama-cpp
- gguf-my-repo
Triangle104/Athena-3-3B-Q5_K_S-GGUF
This model was converted to GGUF format from Spestly/Athena-3-3B using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Athena-3-3B is a 3.09-billion-parameter causal language model fine-tuned from Qwen2.5-3B-Instruct. This model is designed to excel in various natural language processing tasks, offering enhanced reasoning and instruction-following capabilities.
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/Athena-3-3B-Q5_K_S-GGUF --hf-file athena-3-3b-q5_k_s.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Triangle104/Athena-3-3B-Q5_K_S-GGUF --hf-file athena-3-3b-q5_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/Athena-3-3B-Q5_K_S-GGUF --hf-file athena-3-3b-q5_k_s.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Triangle104/Athena-3-3B-Q5_K_S-GGUF --hf-file athena-3-3b-q5_k_s.gguf -c 2048