README.md

---
base_model:
- DewEfresh/neo_7b
- DewEfresh/neo_7b
tags:
- merge
- mergekit
- lazymergekit
- DewEfresh/neo_7b
---

# Neo_7b-merge13

Neo_7b-merge13 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [DewEfresh/neo_7b](https://huggingface.co/DewEfresh/neo_7b)
* [DewEfresh/neo_7b](https://huggingface.co/DewEfresh/neo_7b)

## 🧩 Configuration

```yaml
# Define the slices for the model merging process
slices:
  - sources:
      # First part: merge layer 0 with layer 3
      - model: DewEfresh/neo_7b
        layer_range: [0, 0]
      - model: DewEfresh/neo_7b
        layer_range: [3, 3]
  - sources:
      # Second part: merge layer 1 with layer 3
      - model: DewEfresh/neo_7b
        layer_range: [1, 1]
      - model: DewEfresh/neo_7b
        layer_range: [3, 3]
  - sources:
      # Third part: merge layer 2 with layer 3
      - model: DewEfresh/neo_7b
        layer_range: [2, 2]
      - model: DewEfresh/neo_7b
        layer_range: [3, 3]
  - sources:
      # Fourth part: layers 4 to 27 from the original model
      - model: DewEfresh/neo_7b
        layer_range: [4, 27]
# Specify the merging method for the slices
merge_method: slerp
base_model: DewEfresh/neo_7b
parameters:
  t: 0.3333
dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "DewEfresh/Neo_7b-merge13"
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"])
```