metadata

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-Q4_K_M-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-Q4_K_M-GGUF --hf-file athena-3-3b-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Athena-3-3B-Q4_K_M-GGUF --hf-file athena-3-3b-q4_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/Athena-3-3B-Q4_K_M-GGUF --hf-file athena-3-3b-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Triangle104/Athena-3-3B-Q4_K_M-GGUF --hf-file athena-3-3b-q4_k_m.gguf -c 2048