Triangle104/Rombo-LLM-V3.0-Qwen-32b-Q8_0-GGUF

This model was converted to GGUF format from Rombo-Org/Rombo-LLM-V3.0-Qwen-32b using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

---

Rombo-LLM-V3.0-Qwen-32b is a Continued Finetune model on top of the previous V2.5 version using the "NovaSky-AI/Sky-T1_data_17k" dataset. The resulting model was then merged backed into the base model for higher performance as written in the continuous finetuning technique bellow. This model is a good general purpose model, however it excells at coding and math.

https://docs.google.com/document/d/1OjbjU5AOz4Ftn9xHQrX3oFQGhQ6RDUuXQipnQ9gn6tU/edit?usp=sharing

---

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/Rombo-LLM-V3.0-Qwen-32b-Q8_0-GGUF --hf-file rombo-llm-v3.0-qwen-32b-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Rombo-LLM-V3.0-Qwen-32b-Q8_0-GGUF --hf-file rombo-llm-v3.0-qwen-32b-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 Triangle104/Rombo-LLM-V3.0-Qwen-32b-Q8_0-GGUF --hf-file rombo-llm-v3.0-qwen-32b-q8_0.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Rombo-LLM-V3.0-Qwen-32b-Q8_0-GGUF --hf-file rombo-llm-v3.0-qwen-32b-q8_0.gguf -c 2048