---
pipeline_tag: question-answering
---
# Model Card for Llama3-4x8B-TimeAgents

This model card serves as a detailed guide and reference for users interacting with the Llama3-TimeAgents-4x8B model.

## Model Details

### Model Description

Llama3-TimeAgents-4x8B is an advanced machine learning model specifically developed for multi-agent systems focused on time series predictions. This model harnesses the capabilities of a state-of-the-art language model framework, tailored to assist in research environments where precise and predictive temporal data analysis is crucial. It combines the robustness of transformer architectures with specialized adaptations for handling the dynamics of multi-agent interactions and time-sensitive data, making it an ideal tool for pushing the boundaries of academic and applied research in time series forecasting. This innovative model is the culmination of rigorous research and development efforts aimed at addressing complex challenges in predictive analytics within multi-agent systems.

- **Model type:** Transformer-based
- **Language(s) (NLP):** Primarily English
- **License:** LLama 3 
- **Merged from:** Llama3 base model and variations
- 


### Disclaimer
This model is a research experiment and may generate incorrect or harmful content. The model's outputs should not be taken as factual or representative of the views of the model's creator or any other individual.

The model's creator is not responsible for any harm or damage caused by the model's outputs.

### Direct Use

The model is ready for direct integration into applications requiring natural language understanding and generation without further training or significant modifications.

### Out-of-Scope Use

This model is not intended for use in scenarios requiring highly sensitive or critical decision-making processes, where inaccuracies could lead to significant harm or legal issues.

## Bias, Risks, and Limitations

The model, like all AI language models, may inherit biases from the training data, and its outputs should be carefully reviewed in any sensitive application.

### Recommendations

Users should be aware of the potential for biased outputs and should implement appropriate checks and balances when using the model in production environments.

## How to Get Started with the Model

To get started with the Llama3-TimeAgents-4x8B, you can use the following Python code snippet, which utilizes the `transformers` library:

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

model_id = "AIFS/Llama3-8x8B-Time-Agents/"
tokenizer = AutoTokenizer.from_pretrained(model_id)
chat_pipeline = pipeline(
    "text-generation",
    model=model_id,
    tokenizer=tokenizer,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "How can I create a Restful API?"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = chat_pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```

## Evaluation

### Testing Data, Factors & Metrics

To ensure robust performance, the model was evaluated against a diverse set of benchmarks focusing on language understanding and generation.

#### Metrics

Soon after Fine-Tuning

### Results

Detailed results will be available upon completion of ongoing evaluations.

## Environmental Impact

The environmental impact of training such models is significant. Users and stakeholders are encouraged to consider sustainability in their deployment strategies.

- **Carbon Emitted:** Estimated upon request


Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.

## License
[Meta LLama 3 Licence](https://llama.meta.com/llama3/license/)