---
license:
- apache-2.0
- cc-by-nc-4.0
datasets: pszemraj/fleece2instructions-codealpaca
tags:
- generated_from_trainer
- instruct
- instructions
- code
metrics:
- rouge
language:
- en
widget:
  - text: |
      import torch
      from transformers import AutoTokenizer, AutoModelForSequenceClassification

      checkpoint = "distilbert-base-uncased-finetuned-sst-2-english"
      tokenizer = AutoTokenizer.from_pretrained(checkpoint)
      model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
      sequences = ["I've been waiting for a HuggingFace course my whole life.", "So have I!"]

      tokens = tokenizer(sequences, padding=True, truncation=True, return_tensors="pt")
      output = model(**tokens)
    example_title: Example One

  - text: |
      import torch
      from tqdm.auto import tqdm

      device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
      model.to(device)

      progress_bar = tqdm(range(num_training_steps))

      model.train()
      for epoch in range(num_epochs):
          for batch in train_dataloader:
              batch = {k: v.to(device) for k, v in batch.items()}
              outputs = model(**batch)
              loss = outputs.loss
              loss.backward()

              optimizer.step()
              lr_scheduler.step()
              optimizer.zero_grad()
              progress_bar.update(1)
    example_title: Example Two

  - text: |
      import evaluate

      metric = evaluate.load("glue", "mrpc")
      model.eval()
      for batch in eval_dataloader:
          batch = {k: v.to(device) for k, v in batch.items()}
          with torch.no_grad():
              outputs = model(**batch)

          logits = outputs.logits
          predictions = torch.argmax(logits, dim=-1)
          metric.add_batch(predictions=predictions, references=batch["labels"])

      metric.compute()
    example_title: Example Three

  - text: |
      git lfs install
      huggingface-cli lfs-enable-largefiles .
      git lfs track "*.bin"
      git add .
      git commit -a -m "add fp32 chkpt"
      git push
    example_title: Example Four

  - text: |
      export interface DocumentParams {
        pageContent: string;

        // eslint-disable-next-line @typescript-eslint/no-explicit-any
        metadata: Record<string, any>;
      }

      /**
       * Interface for interacting with a document.
       */
      export class Document implements DocumentParams {
        pageContent: string;

        // eslint-disable-next-line @typescript-eslint/no-explicit-any
        metadata: Record<string, any>;

        constructor(fields?: Partial<DocumentParams>) {
          this.pageContent = fields?.pageContent ?? this.pageContent;
          this.metadata = fields?.metadata ?? {};
        }
      }
    example_title: Example Five
inference:
  parameters:
    max_length: 128
    num_beams: 4
---


# bart-large-code-instructiongen

Use this text2text model to find out what LLM instructions might be able to generate an arbitary piece of code!

This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co./facebook/bart-large) on the `pszemraj/fleece2instructions-codealpaca` dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9222
- Rouge1: 62.0692
- Rouge2: 36.1947
- Rougel: 57.5128
- Rougelsum: 58.6613
- Gen Len: 31.0060


## Intended uses & limitations

🚨 **note:** as the authors elected to release the [original dataset](https://github.com/sahil280114/codealpaca) under `cc-by-nc`, the license carries over to this model and **cannot be used for commercial activity**. 

Intended use: Research on domain adaptation and/or other improvements to LLMs by extending instruction:text data pairs.

## Training and evaluation data

Refer to the linked dataset card for `pszemraj/fleece2instructions-codealpaca` or the [original dataset](https://github.com/sahil280114/codealpaca) repo.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.0914        | 1.0   | 563  | 1.0303          | 60.288  | 34.1884 | 55.9293 | 57.0714   | 30.6267 |
| 0.8688        | 2.0   | 1126 | 0.9333          | 61.0409 | 34.9823 | 56.4887 | 57.6662   | 31.7255 |
| 0.6773        | 3.0   | 1689 | 0.9222          | 62.0692 | 36.1947 | 57.5128 | 58.6613   | 31.0060 |