# Hibernates/Hibernates-2B-R1-V1

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

### Model Overview <a href="#model-overview" id="model-overview"></a>

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 <a href="#key-features" id="key-features"></a>

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

### Technical Specifications <a href="#technical-specifications" id="technical-specifications"></a>

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

### Usage Guide <a href="#usage-guide" id="usage-guide"></a>

```
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 <a href="#performance-characteristics" id="performance-characteristics"></a>

#### Strengths <a href="#strengths" id="strengths"></a>

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

#### Considerations <a href="#considerations" id="considerations"></a>

* 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 <a href="#license-and-usage" id="license-and-usage"></a>

* Research and commercial use permitted
* Attribution appreciated but not required
* No warranty provided

### Citation <a href="#citation" id="citation"></a>

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

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

### Acknowledgments <a href="#acknowledgments" id="acknowledgments"></a>

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

### Download Instructions <a href="#download-instructions" id="download-instructions"></a>

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.


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# Agent Instructions: Querying This Documentation

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Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://hibernates-ai.gitbook.io/hibernates-2b-r1-v1/hibernates-hibernates-2b-r1-v1.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.