README.md

---
tags:
- merge
- mergekit
- lazymergekit
- google-bert/bert-base-uncased
- KM4STfulltext/SSCI-SciBERT-e4
base_model:
- google-bert/bert-base-uncased
- KM4STfulltext/SSCI-SciBERT-e4
license: apache-2.0
pipeline_tag: depth-estimation
---

# natural_science_model

natural_science_model is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased)
* [KM4STfulltext/SSCI-SciBERT-e4](https://huggingface.co/KM4STfulltext/SSCI-SciBERT-e4)

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: google-bert/bert-base-uncased
        layer_range: [0, 32]
      - model: KM4STfulltext/SSCI-SciBERT-e4
        layer_range: [0, 32]
merge_method: slerp
base_model: google-bert/bert-base-uncased
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "nagayama0706/natural_science_model"
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"])
```