Falcon-7b-chat-new-finetune

Falcon-7b-chat-new-finetune

This model is a fine-tuned version of ybelkada/falcon-7b-sharded-bf16 using the timdettmers/openassistant-guanaco dataset. It has been fine-tuned using LoRA (Low-Rank Adaptation), PEFT (Parameter-Efficient Fine-Tuning), and TRL (Transformer Reinforcement Learning) techniques. It also leverages BitsAndBytes for 4-bit quantization.

Model Description

This model is intended for conversational AI tasks, such as chatbots and dialogue systems. It has been trained on a large dataset of human conversations and is capable of generating human-like text.

Intended Uses & Limitations

• Intended uses: This model can be used for text generation, dialogue systems, and other conversational AI applications.
• Limitations: The model may generate biased or offensive content. It is important to carefully review the model's outputs before using them in a production environment.

Training and Fine-tuning

• Base model: ybelkada/falcon-7b-sharded-bf16
• Dataset: timdettmers/openassistant-guanaco
• Fine-tuning techniques: LoRA, PEFT, TRL
• Quantization: BitsAndBytes (4-bit)

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

prompt = "Explain AI vs ML vs DL vs Generative AI."
model_name = "chaitanya42/Falcon-7b-chat-new-finetune"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
result = pipe(f"{prompt} \n")
print(result[0]['generated_text'])

Author

This model was fine-tuned by chaitanya42.