Llama 3 Refueled

🚀 RefuelLLM-2-small (Llama-3-Refueled)

RefuelLLM-2-small, also known as Llama-3-Refueled, is a Llama3-8B base model instruction tuned on a large corpus of datasets, suitable for various NLP tasks such as classification and reading comprehension.

📚 Documentation

Model Details

RefuelLLM-2-small, aka Llama-3-Refueled, is a Llama3-8B base model instruction tuned on a corpus of 2750+ datasets, spanning tasks such as classification, reading comprehension, structured attribute extraction and entity resolution. We're excited to open-source the model for the community to build on top of.

• More details about RefuelLLM-2 family of models
• You can also try out the models in our LLM playground

💻 Usage Examples

Basic Usage

>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer

>>> model_id = "refuelai/Llama-3-Refueled"
>>> tokenizer = AutoTokenizer.from_pretrained(model_id)
>>> model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

>>> messages = [{"role": "user", "content": "Is this comment toxic or non-toxic: RefuelLLM is the new way to label text data!"}]

>>> inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to("cuda")

>>> outputs = model.generate(inputs, max_new_tokens=20)
>>> print(tokenizer.decode(outputs[0]))

🔧 Technical Details

Training Data

The model was both trained on over 4 Billion tokens, spanning 2750+ NLP tasks. Our training collection consists majorly of:

1. Human annotated datasets like Flan, Task Source, and the Aya collection
2. Synthetic datasets like OpenOrca, OpenHermes and WizardLM
3. Proprietary datasets developed or licensed by Refuel AI

Benchmarks

In this section, we report the results for Refuel models on our benchmark of labeling tasks. For details on the methodology see here.

⚠️ Limitations

The Llama-3-Refueled does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.

📄 License

This model is released under the CC BY-NC 4.0 license.