Model Card for Model ID

The model extracts the triplet from the given text. Example: Input: "Nie Haisheng, born on October 13, 1964, worked as a fighter pilot." Output: {'mtriple_set': [['Nie_Haisheng | birthDate | 1964-10-13', 'Nie_Haisheng | occupation | Fighter_pilot']]}

Model Details

Base Model: Llama 3 - 8B Qunatisation: 4 bit LoRA rank: 16

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.

Developed by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

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.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime:

  trainer = SFTTrainer(
    model = model,
     tokenizer = tokenizer,
     train_dataset = train,
     dataset_text_field = "text",
     max_seq_length = max_seq_length,
     dataset_num_proc = 2,
     packing = False, # Can make training 5x faster for short sequences.
     args = TrainingArguments(
         per_device_train_batch_size = 2,
         gradient_accumulation_steps = 4,
         warmup_steps = 5,
         max_steps = 50,
         learning_rate = 2e-4,
         fp16 = not is_bfloat16_supported(),
         bf16 = is_bfloat16_supported(),
         logging_steps = 1,
         optim = "adamw_8bit",
         weight_decay = 0.01,
         lr_scheduler_type = "linear",
         seed = 3407,
         output_dir = "outputs",
    ),
)```
#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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).

- **Hardware Type:** 1 T4 GPU, RAM: 16 GB
- **Hours used:** [More Information Needed]
- **Cloud Provider:** Google CoLab
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing#scrollTo=kR3gIAX-SM2q

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]