README.md

---
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
- Qwen/Qwen2.5-Math-1.5B-Instruct
tags:
- merge
- mergekit
- lazymergekit
- Qwen/Qwen2.5-1.5B-Instruct
- Qwen/Qwen2.5-Math-1.5B-Instruct
---

# test_mod

test_mod is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
* [Qwen/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B-Instruct)

## 🧩 Configuration

```yaml
# Configuration 5: SLERP Method
slices:
  - sources:
      - model: Qwen/Qwen2.5-1.5B-Instruct
        layer_range: [0, 28]
      - model: Qwen/Qwen2.5-Math-1.5B-Instruct
        layer_range: [0, 28]
merge_method: slerp
base_model: Qwen/Qwen2.5-1.5B-Instruct
parameters:
  t:
    - filter: self_attn
      value: 0.1
    - filter: mlp
      value: 0.1
    - value: 0.1
dtype: bfloat16

```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Chilee244/test_mod"
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"])
```