Triangle104/Ava-1.5-12B-Q4_K_S-GGUF

This model was converted to GGUF format from Spestly/Ava-1.5-12B 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:

Ava 1.5

Ava 1.5 is a cutting-edge conversational AI model, fine-tuned from Ava 1.0 to deliver exceptional conversational capabilities. Designed to be your go-to AI for engaging, accurate, and context-aware dialogues, Ava 1.5 incorporates updated knowledge and enhanced natural language understanding to provide an unparalleled user experience.

Key Features

Enhanced Conversational Skills: Ava 1.5 demonstrates fluid and human-like dialogue generation with improved contextual understanding.
Updated Knowledge Base: Trained on the latest datasets, Ava 1.0 ensures responses are relevant and informed.
Multi-Turn Conversation: Handles complex, multi-turn interactions seamlessly, maintaining coherence and focus.
Personalized Assistance: Adapts responses based on user preferences and context.
Multilingual Support: Capable of understanding and responding in multiple languages with high accuracy.

Why Ava 1.5?

Ava 1.5 is built to excel in a wide range of applications:

Customer Support: Provides intelligent, empathetic, and accurate responses to customer queries.
Education: Acts as an interactive tutor, offering explanations and personalized guidance.
Personal Assistance: Supports daily tasks, scheduling, and answering general queries with ease.
Creative Collaboration: Assists with brainstorming, writing, and other creative processes.

Usage

Using Ava 1.5 in your project is straightforward. Here’s a quick setup guide:

Installation

Ensure you have the necessary libraries and dependencies installed. Use the following command:

pip install transformers

Implementation

Here’s a sample Python script to interact with Ava 1.5:

Use a pipeline as a high-level helper

from transformers import pipeline

pipe = pipeline("text-generation", model="Spestly/Ava-1.5-12B")

Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Spestly/Ava-1.5-12B") model = AutoModelForCausalLM.from_pretrained("Spestly/Ava-1.5-12B")

Training Highlights

Ava 1.5 was fine-tuned with the following enhancements:

Extensive Conversational Dataset: Leveraging a wide array of open-domain and specialized conversational datasets.
Knowledge Integration: Incorporating recent advancements and updates to provide cutting-edge insights.
Fine-Tuning on Ava 1.0: Utilizing the powerful Ava 1.0 model to further refine and expand upon the model's ability to perform tasks!

Limitations

Contextual Challenges: In rare cases, Ava 1.0 may misinterpret ambiguous inputs.
Hardware Requirements: Optimal performance requires a robust system with GPU acceleration.

Roadmap

Ava 2.0: Introducing real-time learning capabilities and broader conversational adaptability.
Lightweight Model: Developing a lightweight version optimized for edge devices.
Domain-Specific Fine-Tunes: Specialized versions for industries like healthcare, education, and finance.

Contributing

We welcome contributions to enhance Ava! Here’s how you can get involved:

Fork this repository.
Create a feature branch.
Submit a pull request with detailed explanations of your changes.

License

Ava 1.5 is released under Apache 2.0 License. Please review the LICENSE file for more details.

Contact

For inquiries, feedback, or support, feel free to reach out:

Email: aayan.mishra@proton.me
GitHub: Spestly
Website: Ava Project Page

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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/Ava-1.5-12B-Q4_K_S-GGUF --hf-file ava-1.5-12b-q4_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Ava-1.5-12B-Q4_K_S-GGUF --hf-file ava-1.5-12b-q4_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/Ava-1.5-12B-Q4_K_S-GGUF --hf-file ava-1.5-12b-q4_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Ava-1.5-12B-Q4_K_S-GGUF --hf-file ava-1.5-12b-q4_k_s.gguf -c 2048