library_name: transformers
base_model: rombodawg/Rombos-LLM-V2.5-Qwen-7b
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
Triangle104/Rombos-LLM-V2.5-Qwen-7b-Q6_K-GGUF
This model was converted to GGUF format from rombodawg/Rombos-LLM-V2.5-Qwen-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.
Model details:
Rombos-LLM-V2.5-Qwen-7b is a continues finetuned version of Qwen2.5-7B. I noticed recently that the Qwen team did not learn from my methods of continuous finetuning, the great benefits, and no downsides of it. So I took it upon myself to merge the instruct model with the base model myself using the Ties merge method
This version of the model shows higher performance than the original instruct and base models.
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/Rombos-LLM-V2.5-Qwen-7b-Q6_K-GGUF --hf-file rombos-llm-v2.5-qwen-7b-q6_k.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Triangle104/Rombos-LLM-V2.5-Qwen-7b-Q6_K-GGUF --hf-file rombos-llm-v2.5-qwen-7b-q6_k.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/Rombos-LLM-V2.5-Qwen-7b-Q6_K-GGUF --hf-file rombos-llm-v2.5-qwen-7b-q6_k.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Triangle104/Rombos-LLM-V2.5-Qwen-7b-Q6_K-GGUF --hf-file rombos-llm-v2.5-qwen-7b-q6_k.gguf -c 2048