README.md

# 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.

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## 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.

## Model Developers
This version has been developed independently and is not associated with Meta.

## Input/Output
- **Input**: Text only.
- **Output**: Generates text and code only.

## 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.

## 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.

## Usage Instructions

### Use with transformers
You can leverage the `transformers` library to run inference.

#### Transformers Pipeline

```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.

## 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.