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namratanwani
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information-extraction-llama3-8B-4bit-finetuned

Transformers
Safetensors
English
unsloth
information-extraction
llama3
finetuning
Inference Endpoints
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information-extraction-llama3-8B-4bit-finetuned / README.md
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language:
  - en
library_name: transformers
tags:
  - unsloth
  - information-extraction
  - llama3
  - finetuning

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

Model Sources [optional]

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Uses

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

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

Preprocessing [optional]

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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. -->

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## Evaluation

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

### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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

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#### Metrics

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

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### Results

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#### Summary



## Model Examination [optional]

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

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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]

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

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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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