language: - en

license: apache-2.0

tags: - causal-lm - text-generation - 7b - ozone-ai - reverb - open-source

datasets: []

metrics: - type: MMLU Pro value: 0.4006

Reverb-7b: Ozone AI

Model Description

Reverb-7b is a 7 billion parameter language model developed by Ozone AI. It is a causal language model designed for text generation and various downstream tasks. This is the third model release by Ozone AI.

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https://discord.gg/ozone

Intended Uses & Limitations

Reverb-7b is intended for research and chatting purposes in natural language processing. Potential use cases include:

• Text generation
• Question answering
• Summarization
• Code generation (performance may vary)
• Creative writing

Limitations:

• Like all language models, Reverb-7b can generate biased or harmful content. Users should implement appropriate safeguards to mitigate these risks.
• The model’s performance may vary depending on the specific task and dataset.
• Important Safety Note: We have observed that at lower quantization levels (e.g., below 4-bit), the model’s safety guardrails may be less effective. The model may be more likely to generate inappropriate or harmful content, or to solicit personal information. Exercise extreme caution when using Reverb-7b at these lower quantization levels and implement strict input/output filtering.

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ozone-ai/Reverb-7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "The quick brown fox jumps over the lazy dog."
input_ids = tokenizer(prompt, return_tensors="pt").input_ids

generation_output = model.generate(input_ids, max_length=50)
print(tokenizer.decode(generation_output[0]))

Evaluation

Benchmarks

The following table shows the performance of Reverb-7b on various benchmarks:

Training Details

• Training infrastructure: 1x H100 PCIE
• Training procedure: 1 epoch

Contact

For questions or feedback, please contact us through contact@ozone-ai.com or https://ozone-ai.com

Attribution

Built with Qwen Users of this model must agree with the Qwen license agreement

Model Card Authors

Vneq - CEO @ Ozone AI Tristan - CEO @ ShuttleAI, CTO @ Ozone AI