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---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
- llama3
- llama38b
- 8b
- dolphin
- babydolphin
base_model: unsloth/llama-3-8b-bnb-4bit
datasets:
- cognitivecomputations/dolphin
---
# BabyDolphin-8B-LLaMA3-Uncensored
- **Developed by:** babycommando
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This model, `babydolphin-8b-llama3-uncensored`, is an 8-billion parameter subset of the larger LLaMA (Large Language Model by Meta) and has been fine-tuned on the `cognitivecomputations/dolphin` dataset specifically for the FLAN1M-Alpaca-Uncensored tasks. It incorporates cutting-edge transformer architectures optimized for a balance between performance and efficiency.
## Model Description
`babydolphin-8b-llama3-uncensored` is designed to deliver powerful language understanding and generation capabilities while ensuring compliance with non-censorship standards for diverse application scenarios. This version is ideal for applications requiring high-quality text generation where content restrictions are minimal.
### Technical Details
- **Base Model**: LLaMA3
- **Parameters**: 8 billion
- **Fine-tuning Dataset**: cognitivecomputations/dolphin FLAN1M-Alpaca-Uncensored
### Quantization and Configuration
This model is available in multiple configurations to best suit different deployment needs:
- **f16**: Fastest conversion, retains 100% accuracy but is slow and memory-intensive.
- **q4_k_m**: Recommended for general use, balancing between speed and efficiency.
- **q3_k_m**: Good for environments where model size and speed are more critical than detailed accuracy.
- **q3_k_s**: Maximizes speed and minimizes model size, suitable for very resource-constrained environments.
## Intended Use
This model is intended for researchers and developers needing advanced natural language processing capabilities without censorship restrictions. It is particularly well-suited for generating text in scenarios where nuanced, unrestricted content generation is crucial.
## How to Use
For Ollama, check their docs for [running a GGUF model on Ollama](https://github.com/ollama/ollama/blob/main/docs/import.md)
Here is how to load and use the model in your projects using Hugging Face Transformers:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "babycommando/babydolphin-8b-llama3-uncensored"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
inputs = tokenizer("Hello, world!", return_tensors="pt")
outputs = model.generate(inputs["input_ids"])
print(tokenizer.decode(outputs[0]))
```
### Training Loss Over 60 Epochs
-
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
## Usage
Copy this markdown content into your model's page on the Hugging Face Model Hub to provide users with a clear, informative description of what your model can do and how it can be used. Adjust the `model_name` variable in the Python code snippet to reflect the actual path to your model on Hugging Face for ease of use by others. |