---
language:
- en
license: mit
---

# Llama-3-IMPACTS-2x8B-64k-MLX

---

**Designed for Advanced Problem-Solving Across Interconnected Domains of Biomimicry, Climate Change, and Astrobiology**

The `Llama-3-IMPACTS-2x8B-64k-MLX` model is a cutting-edge large language model trained on the I.M.P.A.C.T.S dataset, which encompasses scenarios from biomimicry, climate change, and theoretical astrobiology. This model has been specifically tailored to generate innovative solutions and insights for both Earth and potential extraterrestrial environments, reflecting key themes of resilience, sustainability, and the interconnectedness of life across the universe.

## Model Details

### Description

- **Model name:** `Llama-3-IMPACTS-2x8B-64k-MLX`
- **Developer:** Severian
- **Version:** 1.0
- **License:** MIT

### Training Data

The model was trained on a subset of the I.M.P.A.C.T. dataset, utilizing 35,000 carefully curated examples that include detailed scenarios involving climate adaptation, biomimetic applications, and the potential for life in varying cosmic conditions.

### Model Architecture

- **Type:** Llama-3
- **Parameters:** 8 billion
- **Training Epochs:** 1 (35K Examples)
- **Context Limit:** 64K

## Intended Uses

This model is intended for use in applications that require deep, interdisciplinary understanding and the generation of novel insights within the realms of environmental science, synthetic biology, space exploration, and sustainability studies. Its capabilities make it ideal for:
- Research and academic studies aiming to explore complex scenarios involving ecological and astrobiological phenomena.
- Organizations looking to innovate in the fields of climate resilience and biomimicry.
- Creative problem-solving in contexts where conventional approaches are insufficient.

## How to Use This Model

The model can be loaded and used in various natural language processing tasks that require nuanced understanding and creative output. Here is a basic example of how to load and use the model using the Hugging Face Transformers library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Severian/Llama-3-IMPACTS-2x8B-64k-MLX"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example prompt
prompt = "How could Bioluminescent Algae come to evolve into a life form around a red dwarf star that has no planets or rocky material? Next, how could that Bioluminescent Algae somehow make it’s way to earth as an alien entity? Then, what would happen over a 100 year span if that alien Bioluminescent Algae led to the over-acidification of the water on the entire planet? hHw could we use biomimicry to stop the ocean from over-acidification?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(inputs["input_ids"], max_length=200)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Limitations and Biases

While the `Llama-3-IMPACTS-2x8B-64k-MLX` model is designed to be a powerful tool for generating insightful content, it inherits limitations from its training data, which, though extensive, may not capture all possible scenarios or biases. Users should be aware of these limitations and consider them when interpreting the model's outputs, especially in decision-making contexts.

## Model Performance

Initial tests indicate that the model performs exceptionally well in tasks that involve complex reasoning and generating innovative solutions based on the scenarios presented in the I.M.P.A.C.T.S dataset. Further evaluation and fine-tuning may be required to optimize performance for specific applications.

The `Llama-3-IMPACTS-2x8B-64k-MLX` model represents an avenue that AI can use for exploring and solving complex problems across multiple domains. By leveraging the rich, interconnected dataset of I.M.P.A.C.T.S, it offers a valuable tool for researchers, innovators, and thinkers aiming to push the boundaries of what's possible in their fields.