transformers safetensors gemma text-generation unintended-consequences conversational autotrain_compatible endpoints_compatible text-generation-inference region:us
---
library_name: transformers
tags: [gemma, unintended-consequences, text-generation]
---

# Model Card for Gemma 2b - Unintended Consequences

This model is a Gemma-based model fine-tuned for generating text related to the unintended consequences of various actions or decisions.

## Model Details

### Model Description

This is a Gemma model fine-tuned for the task of generating text about unintended consequences. The model has been trained on a dataset of text examples that describe various actions and their unintended consequences.

- **Developed by:** Falcons.AI
- **Model type:** GemmaForCausalLM
- **Language(s) (NLP):** English
- **Finetuned from model:** gemma-2b-it

### Model Sources

- **Repository:** [webnizam/gemma-unintended-consequences-2b](https://huggingface.co/webnizam/gemma-unintended-consequences-2b)

## Uses

### Direct Use

This model can be used directly to generate text about unintended consequences of various actions or decisions. It can be useful for brainstorming, risk assessment, or educational purposes.

## How to Get Started with the Model

Use the following code to get started with the model:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("webnizam/gemma-unintended-consequences-2b")
model = AutoModelForCausalLM.from_pretrained("webnizam/gemma-unintended-consequences-2b")

text = "<start_of_text> Outsourcing Manufacturing to Low-Cost Countries"
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```