metadata

language: en
tags:
  - fine-tuned
  - causal-lm
  - instruction-following
model_type: causal-lm
license: mit
datasets:
  - mlabonne/guanaco-llama2-1k
metrics:
  - accuracy
  - loss

Fine-tuned Llama-2 Model on Guanaco Instruction Dataset

Model Description

This model is a fine-tuned version of Llama-2 designed specifically for instruction-following tasks. It has been trained on the Guanaco Llama-2 1k dataset, enabling it to generate coherent and contextually appropriate responses based on given prompts. This model aims to enhance user interactions through improved understanding of instructions and queries.

Intended Use

This model is suitable for various applications, including:

Instruction-following tasks
Chatbot interactions
Text completion based on user prompts
Educational tools for generating explanations or summaries

How to Use

You can easily load this model using the Transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "gautamraj8044/Llama-2-7b-chat-finetune"  

model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
input_text = "Please explain the concept of machine learning."
inputs = tokenizer.encode(input_text, return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))