base_model: nvidia/OpenCodeReasoning-Nemotron-7B
datasets:
- nvidia/OpenCodeReasoning
language:
- en
library_name: transformers
license: apache-2.0
tags:
- nvidia
- code
- llama-cpp
- gguf-my-repo
pipeline_tag: text-generation
Triangle104/OpenCodeReasoning-Nemotron-7B-Q4_K_S-GGUF
This model was converted to GGUF format from nvidia/OpenCodeReasoning-Nemotron-7B using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
OpenCodeReasoning-Nemotron-7B is a large language model (LLM) which is a derivative of Qwen2.5-7B-Instruct (AKA the reference model). It is a reasoning model that is post-trained for reasoning for code generation. The model supports a context length of 32K tokens.
This model is ready for commercial/non-commercial use.
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/OpenCodeReasoning-Nemotron-7B-Q4_K_S-GGUF --hf-file opencodereasoning-nemotron-7b-q4_k_s.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Triangle104/OpenCodeReasoning-Nemotron-7B-Q4_K_S-GGUF --hf-file opencodereasoning-nemotron-7b-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 Triangle104/OpenCodeReasoning-Nemotron-7B-Q4_K_S-GGUF --hf-file opencodereasoning-nemotron-7b-q4_k_s.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Triangle104/OpenCodeReasoning-Nemotron-7B-Q4_K_S-GGUF --hf-file opencodereasoning-nemotron-7b-q4_k_s.gguf -c 2048