shism/NeuralPipe-7B-slerp

---

license: apache-2.0
base_model:
  - OpenPipe/mistral-ft-optimized-1218
  - mlabonne/NeuralHermes-2.5-Mistral-7B
tags:
  - merge
  - mergekit
  - lazymergekit
  - OpenPipe/mistral-ft-optimized-1218
  - mlabonne/NeuralHermes-2.5-Mistral-7B
---

# NeuralPipe-7B-slerp
NeuralPipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/%7B%7B%20model%20%7D%7D)
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/%7B%7B%20model%20%7D%7D)

## 🧩 Configuration

```yaml

slices:

• sources:
  • model: OpenPipe/mistral-ft-optimized-1218 layer_range: [0, 32]
  • model: mlabonne/NeuralHermes-2.5-Mistral-7B layer_range: [0, 32]
• merge_method: slerp base_model: OpenPipe/mistral-ft-optimized-1218 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 = "shism/NeuralPipe-7B-slerp"
    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"])
    ```