metadata

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 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card 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)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

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:

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 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"

./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