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--- |
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language: en |
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tags: |
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- text-generation |
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- transformer |
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- mistral |
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- fine-tuned |
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- uncensored |
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- nsfw |
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license: apache-2.0 |
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datasets: |
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- open-source-texts |
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model-name: Fine-tuned Mistral 7B (Uncensored) |
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--- |
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# Fine-tuned Mistral 7B (Uncensored) |
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## Model Description |
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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. |
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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. |
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## Fine-tuning Process |
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- **Data**: The model was fine-tuned using a dataset of 40,000 textual datapoints sourced from various open-source repositories. |
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- **Training Environment**: Fine-tuning was conducted on two NVIDIA A100 GPUs. |
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- **Training Time**: The training process took approximately 16 hours. |
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- **Optimizer**: The model was trained using AdamW optimizer with a learning rate of `5e-5`. |
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## Intended Use |
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This fine-tuned model is intended for the following tasks: |
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- Text generation |
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- Question answering |
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- Dialogue systems |
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- Content generation for AI-powered interactions, including NSFW or adult-oriented conversations. |
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### How to Use |
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You can easily load and use this model with the `transformers` library in Python: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("your-organization/finetuned-mistral-7b") |
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model = AutoModelForCausalLM.from_pretrained("your-organization/finetuned-mistral-7b") |
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inputs = tokenizer("Input your text here.", return_tensors="pt") |
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outputs = model.generate(inputs["input_ids"], max_length=50, num_return_sequences=1) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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