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Ava-1.0-12B-Q6_K-GGUF

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unsloth
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Ava-1.0-12B-Q6_K-GGUF
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---
base_model: Spestly/Ava-1.0-12B
library_name: transformers
license: apache-2.0
datasets:
- nvidia/HelpSteer2
tags:
- unsloth
- llama-cpp
- gguf-my-repo
---

# Triangle104/Ava-1.0-12B-Q6_K-GGUF
This model was converted to GGUF format from [`Spestly/Ava-1.0-12B`](https://huggingface.co/Spestly/Ava-1.0-12B) 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-1.0-12B) for more details on the model.

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

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
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:
```bash
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](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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
```