Update README.md

README.md

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 This model was converted to GGUF format from [`Spestly/Ava-1.0-8B`](https://huggingface.co/Spestly/Ava-1.0-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/Spestly/Ava-1.0-8B) for more details on the model.
 
+---
+Model details:
+-
+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
+-
+    Compact Yet Powerful:
+        With 8 billion parameters, Ava 1.0 strikes a balance between computational efficiency and performance.
+
+    Enhanced Reasoning Capabilities:
+        Fine-tuned to provide better logical deductions and insightful responses across multiple domains.
+
+    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")
+
+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.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)