Triangle104/AwA-0.5B-Q5_K_S-GGUF

This model was converted to GGUF format from Spestly/AwA-0.5B 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:

AwA (Answers with Athena) is my portfolio project, showcasing a cutting-edge Chain-of-Thought (CoT) reasoning model. I created AwA to excel in providing detailed, step-by-step answers to complex questions across diverse domains. This model represents my dedication to advancing AI’s capability for enhanced comprehension, problem-solving, and knowledge synthesis. Key Features

Chain-of-Thought Reasoning: AwA delivers step-by-step breakdowns of solutions, mimicking logical human thought processes.

Domain Versatility: Performs exceptionally across a wide range of domains, including mathematics, science, literature, and more.

Adaptive Responses: Adjusts answer depth and complexity based on input queries, catering to both novices and experts.

Interactive Design: Designed for educational tools, research assistants, and decision-making systems.

Intended Use Cases

Educational Applications: Supports learning by breaking down complex problems into manageable steps.

Research Assistance: Generates structured insights and explanations in academic or professional research.

Decision Support: Enhances understanding in business, engineering, and scientific contexts.

General Inquiry: Provides coherent, in-depth answers to everyday questions.

Type: Chain-of-Thought (CoT) Reasoning Model

Base Architecture: Adapted from [qwen2]

Parameters: [540m]

Fine-tuning: Specialized fine-tuning on Chain-of-Thought reasoning datasets to enhance step-by-step explanatory capabilities.

Ethical Considerations

Bias Mitigation: I have taken steps to minimise biases in the training data. However, users are encouraged to cross-verify outputs in sensitive contexts.

Limitations: May not provide exhaustive answers for niche topics or domains outside its training scope.

User Responsibility: Designed as an assistive tool, not a replacement for expert human judgment.

Usage

Option A: Local

Using locally with the Transformers library

Use a pipeline as a high-level helper

from transformers import pipeline

messages = [ {"role": "user", "content": "Who are you?"}, ] pipe = pipeline("text-generation", model="Spestly/AwA-0.5B") pipe(messages)

Option B: API & Space

You can use the AwA HuggingFace space or the AwA API (Coming soon!)

Roadmap

More AwA model sizes e.g 7B and 14B
Create AwA API via spestly package

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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/AwA-0.5B-Q5_K_S-GGUF --hf-file awa-0.5b-q5_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/AwA-0.5B-Q5_K_S-GGUF --hf-file awa-0.5b-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/AwA-0.5B-Q5_K_S-GGUF --hf-file awa-0.5b-q5_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/AwA-0.5B-Q5_K_S-GGUF --hf-file awa-0.5b-q5_k_s.gguf -c 2048