---
tags:
- deepseek-ai/deepseek-math-7b-rl
base_model:
- deepseek-ai/deepseek-math-7b-rl
- deepseek-ai/deepseek-math-7b-rl
- deepseek-ai/deepseek-math-7b-rl
- deepseek-ai/deepseek-math-7b-rl
- deepseek-ai/deepseek-math-7b-rl
license: afl-3.0
---

# DeepCode-7B-Aurora-v13

DeepCode-7B-Aurora-v13 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [deepseek-ai/deepseek-math-7b-rl](https://huggingface.co/deepseek-ai/deepseek-math-7b-rl)
* [deepseek-ai/deepseek-math-7b-rl](https://huggingface.co/deepseek-ai/deepseek-math-7b-rl)
* [deepseek-ai/deepseek-math-7b-rl](https://huggingface.co/deepseek-ai/deepseek-math-7b-rl)
* [deepseek-ai/deepseek-math-7b-rl](https://huggingface.co/deepseek-ai/deepseek-math-7b-rl)
* [deepseek-ai/deepseek-math-7b-rl](https://huggingface.co/deepseek-ai/deepseek-math-7b-rl)

## 🧩 Configuration

```yaml
models:
  - model: deepseek-ai/deepseek-math-7b-rl
    # No parameters necessary for base model
  - model: deepseek-ai/deepseek-math-7b-rl
    parameters:
      density: 0.66
      weight: 0.2
  - model: deepseek-ai/deepseek-math-7b-rl
    parameters:
      density: 0.55
      weight: 0.2
  - model: deepseek-ai/deepseek-math-7b-rl
    parameters:
      density: 0.55
      weight: 0.2
  - model: deepseek-ai/deepseek-math-7b-rl
    parameters:
      density: 0.44
      weight: 0.2
  - model: deepseek-ai/deepseek-math-7b-rl
    parameters:
      density: 0.66
      weight: 0.2
merge_method: dare_ties
base_model: deepseek-ai/deepseek-math-7b-rl
parameters:
  int8_mask: true
dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "ALBADDAWI/DeepCode-7B-Aurora-v13"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```