This model is a fine-tuned variant of Meta-Llama-3-8B, adapted using LoRA (Low-Rank Adaptation) techniques. It is designed for text generation tasks and can serve as a backbone for conversational AI, creative writing, or other NLP applications.
This model is built upon Meta-Llama-3-8B and further refined using LoRA adapter weights. It leverages the efficiency and scalability of the Transformers library to provide quality text-generation outputs while reducing computational overhead. The model is particularly useful in scenarios where resource constraints demand a lighter-weight adaptation of larger language models.
This model can be directly used for generating text for chatbots, story generation, and other creative language tasks. It is particularly useful for developers who need an adaptable and efficient language model without the full resource requirements of larger base models.
The model’s architecture allows it to be further fine-tuned for specific tasks such as summarization, translation, or question-answering. Developers can integrate it into larger systems or tailor it to domain-specific applications.
1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model = AutoModelForCausalLM.from_pretrained("dz237/AwareAILabs-v0.11-3-8B")
4tokenizer = AutoTokenizer.from_pretrained("dz237/AwareAILabs-v0.11-3-8B")
5
6# Example usage:
7prompt = "Once upon a time"
8input_ids = tokenizer(prompt, return_tensors="pt").input_ids
9outputs = model.generate(input_ids, max_length=50)
10print(tokenizer.decode(outputs[0], skip_special_tokens=True))
