Triangle104/Qwen2.5-7B-RRP-1M-Q5_K_S-GGUF

This model was converted to GGUF format from bunnycore/Qwen2.5-7B-RRP-1M using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model details:

LoRA trained on a thinking/reasoning and roleplaying dataset and then merged with the Qwen2.5-7B-Instruct-1M model, which supports up to 1 million token context lengths.

What this Model Can Do:

Roleplay: Engage in creative conversations and storytelling! Reasoning: Tackle problems and answer your questions in a logical way (thanks to the LoRA layer). Thinking: Use the tag in your system prompts to activate the model's thinking abilities.

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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/Qwen2.5-7B-RRP-1M-Q5_K_S-GGUF --hf-file qwen2.5-7b-rrp-1m-q5_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Qwen2.5-7B-RRP-1M-Q5_K_S-GGUF --hf-file qwen2.5-7b-rrp-1m-q5_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/Qwen2.5-7B-RRP-1M-Q5_K_S-GGUF --hf-file qwen2.5-7b-rrp-1m-q5_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Qwen2.5-7B-RRP-1M-Q5_K_S-GGUF --hf-file qwen2.5-7b-rrp-1m-q5_k_s.gguf -c 2048