---
base_model: Spestly/Ava-1.0-8B
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- llama-cpp
- gguf-my-repo
license: other
license_name: mrl
license_link: LICENSE
language:
- en
---

# Triangle104/Ava-1.0-8B-Q4_K_S-GGUF
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

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)

```bash
brew install llama.cpp

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

### CLI:
```bash
llama-cli --hf-repo Triangle104/Ava-1.0-8B-Q4_K_S-GGUF --hf-file ava-1.0-8b-q4_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Ava-1.0-8B-Q4_K_S-GGUF --hf-file ava-1.0-8b-q4_k_s.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-1.0-8B-Q4_K_S-GGUF --hf-file ava-1.0-8b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Ava-1.0-8B-Q4_K_S-GGUF --hf-file ava-1.0-8b-q4_k_s.gguf -c 2048
```