base_model, language, library_name, license, license_link, pipeline_tag, tags

base_model	language	library_name	license	license_link	pipeline_tag	tags
Qwen/Qwen2.5-Coder-1.5B-Instruct	en	transformers	apache-2.0	https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct/blob/main/LICENSE	text-generation	code codeqwen chat qwen qwen-coder llama-cpp gguf-my-repo

apto-as/Qwen2.5-Coder-1.5B-Instruct-Q8_0-GGUF

This model was converted to GGUF format from Qwen/Qwen2.5-Coder-1.5B-Instruct using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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 apto-as/Qwen2.5-Coder-1.5B-Instruct-Q8_0-GGUF --hf-file qwen2.5-coder-1.5b-instruct-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo apto-as/Qwen2.5-Coder-1.5B-Instruct-Q8_0-GGUF --hf-file qwen2.5-coder-1.5b-instruct-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 apto-as/Qwen2.5-Coder-1.5B-Instruct-Q8_0-GGUF --hf-file qwen2.5-coder-1.5b-instruct-q8_0.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo apto-as/Qwen2.5-Coder-1.5B-Instruct-Q8_0-GGUF --hf-file qwen2.5-coder-1.5b-instruct-q8_0.gguf -c 2048