Update README.md

README.md

@@ -5,67 +5,38 @@ language:
 pipeline_tag: text-generation
 ---
 # Llama-3-2B-Base
-A slimmed-down, third-party adaptation of the Llama 3 model with only 2 billion parameters.
 
-**Important**: This project is not affiliated with Meta.
-
----
+Llama3-2b is a trimmed version of the official (Llama-3 8B)[https://huggingface.co/meta-llama/Meta-Llama-3-8B] base model from (Meta)[https://huggingface.co/meta-llama].
+It has been reduced in size to ~2 billion parameters, making it more computationally efficient while still retaining a significant portion of the original model's capabilities.
+This model is intended to serve as a base model and has not been further fine-tuned for any specific task.
+It is specifically designed to bring the power of LLMs (Large Language Models) to environments with limited computational resources. This model offers a balance between performance and resource usage, serving as an efficient alternative for users who cannot leverage the larger, resource-intensive versions from Meta.
 
-## Overview
-Llama-3-2B-Base is a reduced version of the popular Llama 3 models, specifically designed to bring the power of LLMs (Large Language Models) to environments with limited computational resources. This model offers a balance between performance and resource usage, serving as an efficient alternative for users who cannot leverage the larger, resource-intensive versions from Meta.
+**Important**: This project is not affiliated with Meta.
 
-## Model Developers
-This version has been developed independently and is not associated with Meta.
+## Uses
 
-## Input/Output
-- **Input**: Text only.
-- **Output**: Generates text and code only.
+This model can be fine-tuned for a variety of natural language processing tasks, including:
 
-## Model Architecture
-Llama-3-2B is an auto-regressive language model using an optimized transformer architecture. It employs supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to enhance alignment with human preferences for helpfulness and safety.
+- Text generation
+- Question answering
+- Sentiment analysis
+- Translation
+- Summarization
 
-## Intended Use
-- **Use Cases**: Suitable for both commercial and research use in English, capable of assistant-like chat and a variety of natural language generation tasks.
-- **Out-of-Scope**: Any use that violates applicable laws or regulations (including trade compliance laws), or the Acceptable Use Policy.
+## Bias, Risks, and Limitations
 
-## Usage Instructions
+While Llama3-2b is a powerful model, it is important to be aware of its limitations and potential biases.
+As with any language model, this model may generate outputs that are factually incorrect or biased.
+It is also possible that the model may produce offensive or inappropriate content.
+Users and Developers should be aware of these risks and take appropriate measures to mitigate them.
 
-### Use with transformers
-You can leverage the `transformers` library to run inference.
+## How to Use
 
-#### Transformers Pipeline
+To use Llama3-2b, you can load the model using the Hugging Face Transformers library in Python:
 
 ```python
-import transformers
-import torch
-
-model_id = "andrijdavid/Llama-3-2B-Base"
-pipeline = transformers.pipeline(
-    "text-generation", model=model_id, 
-    model_kwargs={"torch_dtype": torch.bfloat16}, 
-    device_map="auto"
-)
-
-messages = [
-    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
-    {"role": "user", "content": "Who are you?"}
-]
-
-terminators = [
-    pipeline.tokenizer.eos_token_id, 
-    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
-]
-
-outputs = pipeline(
-    messages, max_new_tokens=256, 
-    eos_token_id=terminators, 
-    do_sample=True, temperature=0.6, top_p=0.9
-)
-print(outputs[0]["generated_text"][-1])
-```
-
-## Hardware and Software Considerations
-Llama-3-2B is designed to run efficiently on mid-tier hardware, significantly lowering the entry barrier for using advanced language models.
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-## Ethical Considerations and Limitations
-Llama-3-2B, like any LLM, is susceptible to generating biased or inappropriate outputs. Developers must evaluate and fine-tune the model to ensure safety and suitability for their specific use cases.
+tokenizer = AutoTokenizer.from_pretrained("andrijdavid/Llama-3-2B-Base/")
+model = AutoModelForCausalLM.from_pretrained("andrijdavid/Llama-3-2B-Base/")
+```