---
base_model: TurkuNLP/gpt3-finnish-3B
license: apache-2.0
datasets:
- TurkuNLP/squad_v2_fi
language:
- fi
pipeline_tag: text-generation
---

# Model Card for Model Futurice/gpt3-finnish-3B-instruct

The model gpt3-finnish-3B-instruct is an instruction fine-tuned model intended for RAG type Q&A in Finnish.

## Model Details

### Model Description

The gpt3-finnish-3B-instruct model is based on TurkuNLP Finnish GPT-3-models. They are a model family of pretrained monolingual GPT-style language models, based on BLOOM-architecture.

The model was fine-tuned using a sample of dataset TurkuNLP/squad_v2_fi, that was DeepL translated from SQuAD2.0.

- **Developed by:** Martti Sutinen
- **Model type:** Bloom
- **Language(s) (NLP):** Finnish
- **License:** Apache-2.0
- **Finetuned from model:** TurkuNLP/gpt3-finnish-large

## Uses

Intended for RAG type Q&A in Finnish.

### Direct Use

Intended for text generation and RAG type Q&A in Finnish. Supply a context and ask a question about it.

### Out-of-Scope Use

Please do not misuse the model. Not recommended for other use cases.

## Bias, Risks, and Limitations

A key limitation is simple and limited selection of fine-tuning data. Please do not expect high quality answers.

### Recommendations

Recommeded to continue fine-tuning with more data or newer architecture.

## How to Get Started with the Model

- Recommended system message: "Olet avustaja. Seuraavaksi saat kysymyksen tai tehtävän. Kirjoita vastaus parhaasi mukaan siten että se täyttää kysymyksen tai tehtävän vaatimukset."
- Recommended format for question about context: Tausta: "{context} \n\nKäytä vain taustaa ja vastaa kysymykseen tai tehtävään: {question}"
- Prompt format: tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

Where messages with typical format:
messages = [
    {"role": "system", "content": system_message},
    {"role": "user", "content": prompt_with_context}
].

Here is what the input could look like: 

\<s><|im_start|>system  
Olet avustaja. Seuraavaksi saat kysymyksen tai tehtävän. Kirjoita vastaus parhaasi mukaan siten että se täyttää kysymyksen tai tehtävän vaatimukset.<|im_end|>  
<|im_start|>user  
Tausta:  
Dokumentti luotiin tammikuussa. Sen kirjoittajaa ei tunneta.  
  
Käytä vain taustaa ja vastaa kysymykseen tai tehtävään: Milloin dokumentti kirjoitettiin?<|im_end|>  
<|im_start|>assistant  


Use pipeline with task text-generation and the recommended format.

## Training Details

### Training Data

Trained with 20000 random samples from test data in: [TurkuNLP/squad_v2_fi](https://huggingface.co/datasets/TurkuNLP/squad_v2_fi).

### Training Procedure

Training was done for 4-bit base model with supervised fine-tuning and Lora.

#### Training Hyperparameters

- **Training regime:** 4-bit, batch size 2, max steps 20000, data collator for completion only

## Evaluation

Evaluation has not been done properly yet.

### Testing Data, Factors & Metrics

#### Testing Data

Evaluated with 1000 random samples from test data in: [TurkuNLP/squad_v2_fi](https://huggingface.co/datasets/TurkuNLP/squad_v2_fi).

#### Factors

Same factors as in SQuAD2.0.

#### Metrics

Loss.

### Results

No results to be shared yet.

#### Summary

## Environmental Impact

Environmental impact not yet evaluated.

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:** Mostly trained on A100
- **Hours used:** 5-10 hours
- **Cloud Provider:** GCP
- **Compute Region:** Unknown
- **Carbon Emitted:** Not evaluated

### Model Architecture and Objective

Bloom.

### Compute Infrastructure

Colab.

#### Hardware

1 x A100.

#### Software

Typical software used.

## Model Card Contact

Martti Sutinen