Triangle104/GWQ-9B-Preview2-Q8_0-GGUF

This model was converted to GGUF format from prithivMLmods/GWQ-9B-Preview2 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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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.

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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/GWQ-9B-Preview2-Q8_0-GGUF --hf-file gwq-9b-preview2-q8_0.gguf -p "The meaning to life and the universe is"

Server:

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 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