metadata

license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct/blob/main/LICENSE
language:
  - en
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
  - code
  - codeqwen
  - chat
  - qwen
  - qwen-coder
  - llama-cpp
  - gguf-my-repo

Triangle104/Qwen2.5-Coder-7B-Instruct-Q4_K_M-GGUF

This model was converted to GGUF format from Qwen/Qwen2.5-Coder-7B-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.

Model details:

UPDATED: 10/11/2024

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/Qwen2.5-Coder-7B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-coder-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Qwen2.5-Coder-7B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-coder-7b-instruct-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/Qwen2.5-Coder-7B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-coder-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Triangle104/Qwen2.5-Coder-7B-Instruct-Q4_K_M-GGUF --hf-file qwen2.5-coder-7b-instruct-q4_k_m.gguf -c 2048