Kelmeilia/llama1_1chat-dialogsum-finetuned

Model Card for Model ID

This model summarizes dialogues between two persons.

This is a sample input for the model:

  
Instruct: Summarize the following conversation.
#Person1#: Happy Birthday, this is for you, Brian.
#Person2#: I'm so happy you remember, please come in and enjoy the party. Everyone's here, I'm sure you have a good time.
#Person1#: Brian, may I have a pleasure to have a dance with you?
#Person2#: Ok.
#Person1#: This is really wonderful party.
#Person2#: Yes, you are always popular with everyone. and you look very pretty today.
#Person1#: Thanks, that's very kind of you to say. I hope my necklace goes with my dress, and they both make me look good I feel.
#Person2#: You look great, you are absolutely glowing.
#Person1#: Thanks, this is a fine party. We should have a drink together to celebrate your birthday

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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Uses

Format dialogue in accord to the sample prompt and you get a summary of the dialogue

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Kelmeilia/llama1_1chat-dialogsum-finetuned"
model = AutoModelForCausalLM.from_pretrained(model_name, device_map='auto', torch_dtype=torch.float16, is_trainable=False)

eval_tokenizer = AutoTokenizer.from_pretrained(base_model_id, add_bos_token=True, trust_remote_code=True, use_fast=False)
eval_tokenizer.pad_token = eval_tokenizer.eos_token

dialogue = """ Joona: Can I have a banana, Ivana?
Ivana: No, I am out of bananas.
Joona: Give me an apple then?
Ivana: Sure, here you go
"""

prompt = f"Instruct: Summarize the following conversation.\n{dialogue}\nOutput:\n"

tokens = eval_tokenizer(p, return_tensors="pt")
result = model.generate(**tokens.to("cuda"), max_new_tokens=100, do_sample=True,num_return_sequences=1,temperature=0.1,num_beams=1,top_p=0.95,).to('cpu')
output = eval_tokenizer.batch_decode(result, skip_special_tokens=True)



dialogue_summary_str = output[0].split('Output:\n')[1]

print(dialogue_summary_str)

Training Details

500 steps of Lora Finetuning

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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