Triangle104/Ava-1.0-12B-Q6_K-GGUF

This model was converted to GGUF format from Spestly/Ava-1.0-12B 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 1.0

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

Key Features

Enhanced Conversational Skills: Ava 1.0 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 1.0?

Ava 1.0 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 1.0 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 1.0:

Use a pipeline as a high-level helper

from transformers import pipeline

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

#OR

Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

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

Training Highlights

Ava 1.0 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 Mistral NeMo: Utilizing the powerful Mistral NeMo framework for robust and efficient training.

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.

License

Ava 1.0 is released under the Apache 2.0 license.

Contact

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

Email: [email protected]
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-1.0-12B-Q6_K-GGUF --hf-file ava-1.0-12b-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Ava-1.0-12B-Q6_K-GGUF --hf-file ava-1.0-12b-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/Ava-1.0-12B-Q6_K-GGUF --hf-file ava-1.0-12b-q6_k.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Ava-1.0-12B-Q6_K-GGUF --hf-file ava-1.0-12b-q6_k.gguf -c 2048