---
library_name: transformers
datasets:
- neil-code/dialogsum-test
base_model:
- TinyLlama/TinyLlama-1.1B-Chat-v1.0
pipeline_tag: summarization
---

# Model Card for Model ID

This model summarizes dialogues between two persons.

This is a sample input for the model:

<PRE>
  
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

</PRE>
## Model Details

### Model Description

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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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### Model Sources [optional]

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## Uses

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

### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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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

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

### Model Architecture and Objective

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