babydolphin-8b-llama3-uncensored

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

Here is how to load and use the model in your projects using Hugging Face Transformers:

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 and Huggingface's TRL library.

<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>

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.