Create README.md

README.md

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+---
+language: en
+tags:
+- text-generation
+- transformer
+- mistral
+- fine-tuned
+- uncensored
+- nsfw
+license: apache-2.0
+datasets:
+- open-source-texts
+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))