---
library_name: transformers
license: mit
---

# caliburn 12b-merged

<!-- Provide a quick summary of what the model is/does. -->

This model is a 12 billion parameter language model created by merging multiple existing models using the MergeKit library. It is designed for general text generation tasks.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is a large language model with 12 billion parameters, created by merging multiple pre-existing models using the MergeKit library. The model is based on the transformer architecture and is fine-tuned for general text generation tasks.

- **Developed by:** The user who created this merged model
- **Model type:** Transformer-based language model
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** Multiple source models merged using MergeKit

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** N/A
- **Demo [optional]:** N/A

## Uses

### Direct Use

This model can be used for various natural language processing tasks, including:

- Text generation
- Code completion
- Question answering
- Summarization

### Downstream Use [optional]

The model can be fine-tuned for specific tasks or domains to improve performance on targeted applications.

### Out-of-Scope Use

This model should not be used for generating harmful, biased, or unethical content. It should not be relied upon for critical decision-making without human oversight.

## Bias, Risks, and Limitations

- The model may inherit biases present in its training data or source models.
- It may generate incorrect or nonsensical information.
- The model's outputs should be carefully reviewed and fact-checked.

### Recommendations

Users should be aware of the model's limitations and potential biases. It's recommended to use the model with appropriate content filtering and human oversight, especially for public-facing applications.

## How to Get Started with the Model

Use the following code to get started with the model:

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

tokenizer = AutoTokenizer.from_pretrained("./models/12b-merged")
model = AutoModelForCausalLM.from_pretrained("./models/12b-merged", torch_dtype=torch.float16).to("cuda")

prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs.to("cuda"), max_new_tokens=100)
result = tokenizer.batch_decode(outputs, skip_special_tokens=True)
print(result)