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Ava-1.0-8B

Text Generation
Transformers
PyTorch
English
mistral
text-generation-inference
unsloth
trl
conversational
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Ava-1.0-8B / README.md
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metadata
base_model: mistralai/Ministral-8B-Instruct-2410
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - mistral
  - trl
license: other
license_name: mrl
license_link: LICENSE
language:
  - en

Header

Ava 1.0

Ava 1.0 is an advanced AI model fine-tuned on the Mistral architecture, featuring 8 billion parameters. Designed to be smarter, stronger, and swifter, Ava 1.0 excels in tasks requiring comprehension, reasoning, and language generation, making it a versatile solution for various applications.

Key Features

  1. Compact Yet Powerful:

    • With 8 billion parameters, Ava 1.0 strikes a balance between computational efficiency and performance.

  2. Enhanced Reasoning Capabilities:

    • Fine-tuned to provide better logical deductions and insightful responses across multiple domains.

  3. Optimized for Efficiency:

    • Faster inference and reduced resource requirements compared to larger models.

Use Cases

  • Conversational AI: Natural and context-aware dialogue generation.
  • Content Creation: Generate articles, summaries, and creative writing.
  • Educational Tools: Assist with problem-solving and explanations.
  • Data Analysis: Derive insights from structured and unstructured data.

Technical Specifications

  • Model Architecture: Ministral-8B-Instruct-2410
  • Parameter Count: 8 Billion
  • Training Dataset: A curated dataset spanning diverse fields, including literature, science, technology, and general knowledge.
  • Framework: Hugging Face Transformers

Usage

To use Ava 1.0, integrate it into your Python environment with Hugging Face's 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/Ava-1.0-8B")
pipe(messages)  

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Spestly/Ava-1.0-8B")
model = AutoModelForCausalLM.from_pretrained("Spestly/Ava-1.0-8B")

Performance Benchmarks

Metric Value
Inference Speed 2x faster than Ava 1.0 (12B model)
Accuracy (Benchmarks) 90% on standard NLP tasks
Resource Utilization Reduced memory footprint by 30%

Future Plans

  • Continued optimization for domain-specific applications.
  • Expanding the model's adaptability and generalization capabilities.

Contributing

We welcome contributions and feedback to improve Ava 1.0. If you'd like to get involved, please reach out or submit a pull request.

License

This model is licensed under Mistral Research License. Please review the license terms before usage.