README.md

---
license: apache-2.0
language:
- en
metrics:
- accuracy
base_model:
- meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: reinforcement-learning
---
# Hibernates-2B-R1-V1

A highly efficient 2B parameter language model optimized for reasoning and dialogue tasks.

## Model Overview

Hibernates-2B is a custom transformer architecture designed for advanced language understanding and generation. Built with performance and efficiency in mind, it leverages state-of-the-art techniques for natural language processing.

### Key Features
- 2B Parameters
- 4096 Token Context Window
- Custom Transformer Architecture
- Optimized for CPU and GPU Inference
- Multi-Turn Dialogue Support

## Technical Specifications

- **Architecture**: Custom Transformer
- **Parameters**: 2 Billion
- **Context Length**: 4096 tokens
- **Model Type**: Decoder-only
- **Tokenizer**: Custom WordPiece
- **Format**: SafeTensors

## Usage Guide

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model_id = "Hibernates-2B-R1-V1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Example conversation
messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "How can you help me today?"}
]

# Generate response
input_text = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(
    inputs["input_ids"],
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.95
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
```

## Performance Characteristics

### Strengths
- Efficient Resource Usage
- Strong Reasoning Capabilities
- Multi-Turn Dialogue
- Context Awareness
- Instruction Following

### Considerations
- Resource Requirements: 8GB+ GPU RAM recommended
- Task Specificity: Best suited for dialogue and reasoning tasks
- Language Support: Primary focus on English
- Model Size: Optimized for balance of performance and efficiency

## License and Usage
- Research and commercial use permitted
- Attribution appreciated but not required
- No warranty provided

## Citation

If you use this model in your research, please cite:

```bibtex
@software{hibernates2b_2024,
  title={Hibernates-2B: Efficient Language Model for Reasoning},
  year={2024},
  version={R1-V1}
}
```

## Acknowledgments
Built using PyTorch and Hugging Face Transformers. Special thanks to the open-source AI community.

## Download Instructions

Due to file size limitations, the model files are hosted externally. Download them from:

1. [model-00001-of-00002.safetensors](https://huggingface.co/HibernatesAI/Hibernates-2B-R1-V1/blob/main/model-00001-of-00002.safetensors)
2. [model-00002-of-00002.safetensors](https://huggingface.co/HibernatesAI/Hibernates-2B-R1-V1/blob/main/model-00002-of-00002.safetensors)

Place these files in the root directory of the project before running.