README.md

---
base_model:
- shastraai/Shastra-Mistral-Math-DPO
- shastraai/Shastra-Mistral-Commonsense-SFT-I
tags:
- merge
- mergekit
- lazymergekit
- shastraai/Shastra-Mistral-Math-DPO
- shastraai/Shastra-Mistral-Commonsense-SFT-I
---

# Shastra-Mistral-Math-Commonsense-TIES

Shastra-Mistral-Math-Commonsense-TIES is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [shastraai/Shastra-Mistral-Math-DPO](https://huggingface.co/shastraai/Shastra-Mistral-Math-DPO)
* [shastraai/Shastra-Mistral-Commonsense-SFT-I](https://huggingface.co/shastraai/Shastra-Mistral-Commonsense-SFT-I)

## 🧩 Configuration

```yaml
models:
  - model: shastraai/Shastra-Mistral-Math-DPO
    # no parameters necessary for base model
  - model: shastraai/Shastra-Mistral-Math-DPO
    parameters:
      density: 0.5
      weight: 0.5
  - model: shastraai/Shastra-Mistral-Commonsense-SFT-I
    parameters:
      density: 0.5
      weight: 0.3
merge_method: ties
base_model: shastraai/Shastra-Mistral-Math-DPO
parameters:
  normalize: true
dtype: float16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "shastraai/Shastra-Mistral-Math-Commonsense-TIES"
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"])
```