Update README.md

README.md

@@ -9,15 +9,79 @@ tags:
 - llama
 - trl
 - sft
+- llama3
+- llama38b
+- 8b
+- dolphin
+- babydolphin
 base_model: unsloth/llama-3-8b-bnb-4bit
+datasets:
+- cognitivecomputations/dolphin
 ---
 
-# Uploaded  model
+
+
+![image/webp](https://cdn-uploads.huggingface.co/production/uploads/6305880b2a359dee8a01dccd/kngQqS2VkDb5q5LEovp6v.webp)
+
+# 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
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6305880b2a359dee8a01dccd/RAakycn0OTxHHY26qZLX-.png)
+
+
+- 
+
 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.