license: apache-2.0
datasets:
- sequelbox/Celestia3-DeepSeek-R1-0528
base_model: prithivMLmods/SmolLM2-Rethink-360M
library_name: transformers
language:
- en
pipeline_tag: text-generation
tags:
- trl
- text-generation-inference
- r1
- re-think
- llama-cpp
- gguf-my-repo
Triangle104/SmolLM2-Rethink-360M-Q6_K-GGUF
This model was converted to GGUF format from prithivMLmods/SmolLM2-Rethink-360M using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
SmolLM2-Rethink-360M is an experimental lightweight reasoning model trained on the Celestia3-DeepSeek-R1-0528 dataset. Built on top of the SmolLM2-135M-Instruct architecture and scaled to 360M parameters, it is designed to enhance lightweight reasoning, logical deduction, and structured response generation—all while maintaining efficiency for resource-constrained environments.
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/SmolLM2-Rethink-360M-Q6_K-GGUF --hf-file smollm2-rethink-360m-q6_k.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Triangle104/SmolLM2-Rethink-360M-Q6_K-GGUF --hf-file smollm2-rethink-360m-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/SmolLM2-Rethink-360M-Q6_K-GGUF --hf-file smollm2-rethink-360m-q6_k.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Triangle104/SmolLM2-Rethink-360M-Q6_K-GGUF --hf-file smollm2-rethink-360m-q6_k.gguf -c 2048