transformers safetensors mistral text-generation transformer fine-tuned uncensored nsfw conversational en dataset:open-source-texts license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
datasets:
- open-source-texts
language: en
license: apache-2.0
tags:
- text-generation
- transformer
- mistral
- fine-tuned
- uncensored
- nsfw
model-name: Fine-tuned Mistral 7B (Uncensored)
---

# Fine-tuned Mistral 7B (Uncensored)

## Model Description

This model is a fine-tuned version of the **Mistral 7B**, a dense transformer model, trained on 40,000 datapoints of textual data from a variety of open-source sources. The base model, Mistral 7B, is known for its high efficiency in processing text and generating meaningful, coherent responses.

This fine-tuned version has been optimized for tasks involving natural language understanding, generation, and conversation-based interactions. Importantly, this model is **uncensored**, which means it does not filter or restrict content, allowing it to engage in more "spicy" or NSFW conversations.

## Fine-tuning Process

- **Data**: The model was fine-tuned using a dataset of 40,000 textual datapoints sourced from various open-source repositories.
- **Training Environment**: Fine-tuning was conducted on two NVIDIA A100 GPUs.
- **Training Time**: The training process took approximately 16 hours.
- **Optimizer**: The model was trained using AdamW optimizer with a learning rate of `5e-5`.

## Intended Use

This fine-tuned model is intended for the following tasks:
- Text generation
- Question answering
- Dialogue systems
- Content generation for AI-powered interactions, including NSFW or adult-oriented conversations.

### How to Use

You can easily load and use this model with the `transformers` library in Python:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("your-organization/finetuned-mistral-7b")
model = AutoModelForCausalLM.from_pretrained("your-organization/finetuned-mistral-7b")

inputs = tokenizer("Input your text here.", return_tensors="pt")
outputs = model.generate(inputs["input_ids"], max_length=50, num_return_sequences=1)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
