README.md

---
library_name: transformers
tags:
- sft
- rag
- instruct
- programming
- code
- python
- typescript
license: mit
datasets:
- HuggingFaceFW/fineweb
- glaiveai/glaive-code-assistant-v3
- JuanjoLopez19/Software-Engineering-Dataset_90_10_EN
- MaziyarPanahi/WizardLM_evol_instruct_V2_196k
- tomasonjo/text2cypher-gpt4o-clean
- openbmb/UltraInteract_sft
- Isaak-Carter/Openai-function-invocations-20k-with-greetings
- OpenAssistant/oasst1
- Enoch2090/github_semantic_search
- codeparrot/github-code
- THUDM/AgentInstruct
- mhhmm/typescript-instruct-20k
- petrpan26/typescript-code
- bleugreen/typescript-chunks
- Agent-Eval-Refine/Agent-Trajectories
- mt1234/BTC_USDT_2017-2024
- gradio/custom-component-gallery-backups
- freddyaboulton/gradio-image-urls
- nateraw/gradio-guides-files
- ChobPT/gradio_docs_alpaca
- Gourieff/ReActor
- Hardik1234/reactjs_labelled
- SamSaver/react-issues
- glaiveai/glaive-function-calling-v2
- mzbac/function-calling-llama-3-format-v1.1
- hiyouga/glaive-function-calling-v2-sharegpt
- Trelis/function_calling_v3
- arxiv_dataset
- mteb/raw_arxiv
- CShorten/ML-ArXiv-Papers
- ArtifactAI/arxiv-math-instruct-50k
- totally-not-an-llm/open_gpt2-chatbot
- andfanilo/streamlit-issues
- jacobgoldenart/streamlit-docs
- Harelix/Prompt-Injection-Mixed-Techniques-2024
- thomaserhel/ethusdt-binance-spot-kline-1m-daily-2023-2024
- Chat-Error/Super-good-instruction-data
language:
- en
metrics:
- code_eval
- f1
- perplexity
- bleu
- rouge
- meteor
pipeline_tag: text2text-generation
---
**Model Card for acecalisto3/PhiCo-D-Instruck**

Library Name: transformers

Tags: trl, sft

---
# Model Card for acecalisto3/PhiCo-D-Instruck

This model card summarizes the key information about the `acecalisto3/PhiCo-D-Instruck` model, a 🤗 transformers model available on the Hugging Face Model Hub.

## Model Details

### Model Description

The `acecalisto3/PhiCo-D-Instruck` model is a fine-tuned variant of the `t5-base` model, specifically adapted for InstrucText's instruction following task. It is a seq2seq model with 12 layers, 768 hidden units, and 12 attention heads.

- **Developed by:** [AceCalisto3](https://huggingface.co/acecalisto3)
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [AceCalisto3](https://huggingface.co/acecalisto3)
- **Model type:** T5-base
- **Language(s) (NLP):** English
- **License:** [Apache-2.0](https://github.com/AceCalisto3/PhiCo-D-Instruck/blob/main/LICENSE)
- **Finetuned from model [optional]:** [t5-base](https://huggingface.co/t5-base)

### Model Sources

- **Repository:** [PhiCo-D-Instruck](https://github.com/AceCalisto3/PhiCo-D-Instruck)
- **Paper [optional]:** [PhiCo-D: A Comprehensive Dataset for Instruction Following and Code Generation](https://arxiv.org/abs/2305.11212)
- **Demo [optional]:** [More Information Needed]

## Uses

### Direct Use

The `acecalisto3/PhiCo-D-Instruck` model can be used for instruction following tasks, where it generates responses based on a given context and set of instructions.

### Downstream Use

This model can be fine-tuned for additional downstream tasks such as code generation, dialogue systems, and other applications requiring the understanding and generation of natural language text.

### Out-of-Scope Use

The `acecalisto3/PhiCo-D-Instruck` model is not suitable for tasks that require understanding context beyond the given instructions, such as general world knowledge or domain-specific knowledge.

## Bias, Risks, and Limitations

### Data Bias

The model may exhibit biases inherited from the training data. The PhiCo-D dataset, while extensive, may not cover all possible scenarios and contexts.

### Limitations

The model's responses are based on the given context and instructions. It may not perform well if the context or instructions are unclear, ambiguous, or incomplete.

### Recommendations

Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model.

## How to Get Started with the Model

To get started with the `acecalisto3/PhiCo-D-Instruck` model, you can use the following code snippet:

```python
from transformers import T5ForConditionalGeneration, T5Tokenizer

model = T5ForConditionalGeneration.from_pretrained("acecalisto3/PhiCo-D-Instruck")
tokenizer = T5Tokenizer.from_pretrained("acecalisto3/PhiCo-D-Instruck")

context = "Your context goes here."
instructions = "Your instructions go here."

inputs = tokenizer.encode(f"{context} {instructions}", return_tensors="pt")
outputs = model.generate(inputs, max_length=50, num_beams=5, early_stopping=True)

response = tokenizer.decode(outputs[0])
print(response)
```

## Training Details

### Training Data

[PhiCo-D Dataset Card](https://huggingface.co/datasets/PhiCo-D)

### Training Procedure

#### Preprocessing

- Tokenization: The data was tokenized using the T5 tokenizer.

#### Training Hyperparameters

- Training regime: fp16

#### Speeds, Sizes, Times

- Number of training epochs: 5
- Total training time: 2 days
- Average time per batch: 1.5 seconds

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

[PhiCo-D Testing Data](https://huggingface.co/datasets/PhiCo-D)

#### Factors

- Diversity of contexts and instructions

#### Metrics

- BLEU-4
- ROUGE-L
- METEOR

### Results

#### Summary

| Metric    | Score |
|-----------|-------|
| BLEU-4    | 0.41  |
| ROUGE-L   | 0.52  |
| METEOR    | 0.45  |

## Model Examination

[PhiCo-D Model Interpretability](https://huggingface.co/acecalisto3/PhiCo-D-Instruck/blob/main/interpretability.md)

## Environmental Impact

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:** NVIDIA V100
- **Hours used:** 48
- **Cloud Provider:** Google Cloud
- **Compute Region:** us-central1
- **Carbon Emitted:** 3200 grams of CO2eq

## Technical Specifications

### Model Architecture and Objective

The `acecalisto3/PhiCo-D-Instruck` model is based on the T5-base model architecture with a seq2seq objective.

### Compute Infrastructure

#### Hardware

- NVIDIA V100
- 16 GB GPU memory

#### Software

- PyTorch 1.11
- Transformers 4.20
- CUDA 11.3

## Citation

**BibTeX:**

```bibtex
@misc{PhiCo-D,
    author = {AceCalisto3},
    title = {PhiCo-D-Instruck: A Fine-Tuned T5 Model for Instruction Following},
    howpublished = {\url{https://huggingface.co/acecalisto3/PhiCo-D-Instruck}},
    year = {2023},
    note = {[License: Apache-2.0]},
}
```

**APA:**

AceCalisto3. (2023). PhiCo-D-Instruck: A Fine-Tuned T5 Model for Instruction Following. Retrieved from [https://huggingface.co/acecalisto3/PhiCo-D-Instruck](https://huggingface.co/acecalisto3/PhiCo-D-Instruck)

## Glossary

- **seq2seq:** Sequence-to-sequence models are used to transform one sequence into another sequence.

## More Information

For more information, visit the [PhiCo-D Github repository](https://github.com/AceCalisto3/PhiCo-D).

## Model Card Authors

[AceCalisto3](https://huggingface.co/acecalisto3)

## Model Card Contact

For questions or concerns, please contact [AceCalisto3](https://huggingface.co/acecalisto3) through their Hugging Face profile.