--- license: creativeml-openrail-m language: - en base_model: prithivMLmods/GWQ-9B-Preview2 pipeline_tag: text-generation library_name: transformers tags: - gemma2 - text-generation-inference - f16 - llama-cpp - gguf-my-repo --- # Triangle104/GWQ-9B-Preview2-Q8_0-GGUF This model was converted to GGUF format from [`prithivMLmods/GWQ-9B-Preview2`](https://huggingface.co./prithivMLmods/GWQ-9B-Preview2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co./spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co./prithivMLmods/GWQ-9B-Preview2) for more details on the model. --- Chain of Continuous Thought Synthetic Dataset, which enhances its ability to perform reasoning, multi-step problem solving, and logical inferences. Text Generation: The model is ideal for creative writing tasks such as generating poems, stories, and essays. It can also be used for generating code comments, documentation, and markdown files. Instruction Following: GWQ’s instruction-tuned variant is suitable for generating responses based on user instructions, making it useful for virtual assistants, tutoring systems, and automated customer support. Domain-Specific Applications: Thanks to its modular design and open-source nature, the model can be fine-tuned for specific tasks like legal document summarization, medical record analysis, or financial report generation. Limitations of GWQ2 Resource Requirements: Although lightweight compared to larger models, the 9B parameter size still requires significant computational resources, including GPUs with large memory for inference. Knowledge Cutoff: The model’s pre-training data may not include recent information, making it less effective for answering queries on current events or newly developed topics. Bias in Outputs: Since the model is trained on publicly available datasets, it may inherit biases present in those datasets, leading to potentially biased or harmful outputs in sensitive contexts. Hallucinations: Like other large language models, GWQ can occasionally generate incorrect or nonsensical information, especially when asked for facts or reasoning outside its training scope. Lack of Common-Sense Reasoning: While GWQ is fine-tuned for reasoning, it may still struggle with tasks requiring deep common-sense knowledge or nuanced understanding of human behavior and emotions. Dependency on Fine-Tuning: For optimal performance on domain-specific tasks, fine-tuning on relevant datasets is required, which demands additional computational resources and expertise. Context Length Limitation: The model’s ability to process long documents is limited by its maximum context window size. If the input exceeds this limit, truncation may lead to loss of important information. --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/GWQ-9B-Preview2-Q8_0-GGUF --hf-file gwq-9b-preview2-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/GWQ-9B-Preview2-Q8_0-GGUF --hf-file gwq-9b-preview2-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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/GWQ-9B-Preview2-Q8_0-GGUF --hf-file gwq-9b-preview2-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/GWQ-9B-Preview2-Q8_0-GGUF --hf-file gwq-9b-preview2-q8_0.gguf -c 2048 ```