---
library_name: transformers
tags:
- unsloth
- information-extraction
- llama3
- finetuning
language:
- en
---

# 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

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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.

- **Developed by:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### 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.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:**

```max_seq_length = 2000
  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]

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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

<!-- 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

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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

**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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