SmallThinker-4BA0.6B-Instruct-REAP-0.125-Q8_0-GGUF/README.md

license, base_model, library_name, pipeline_tag, tags

license	base_model	library_name	pipeline_tag	tags
apache-2.0	sasa2000/SmallThinker-4BA0.6B-Instruct-REAP-0.125	transformers	text-generation	text-generation moe pruning reap safetensors custom-code smallthinker llama-cpp gguf-my-repo

sasa2000/SmallThinker-4BA0.6B-Instruct-REAP-0.125-Q8_0-GGUF

This model was converted to GGUF format from sasa2000/SmallThinker-4BA0.6B-Instruct-REAP-0.125 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Code:https://github.com/sasa200004/reap-smallthinker

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 sasa2000/SmallThinker-4BA0.6B-Instruct-REAP-0.125-Q8_0-GGUF --hf-file smallthinker-4ba0.6b-instruct-reap-0.125-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo sasa2000/SmallThinker-4BA0.6B-Instruct-REAP-0.125-Q8_0-GGUF --hf-file smallthinker-4ba0.6b-instruct-reap-0.125-q8_0.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 sasa2000/SmallThinker-4BA0.6B-Instruct-REAP-0.125-Q8_0-GGUF --hf-file smallthinker-4ba0.6b-instruct-reap-0.125-q8_0.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo sasa2000/SmallThinker-4BA0.6B-Instruct-REAP-0.125-Q8_0-GGUF --hf-file smallthinker-4ba0.6b-instruct-reap-0.125-q8_0.gguf -c 2048