README.md

---
license: cc-by-nc-4.0
tags:
- merge
- mergekit
- lazymergekit
- weezywitasneezy/BenchmarkEngineering-7B-slerp
- senseable/WestLake-7B-v2
base_model:
- weezywitasneezy/BenchmarkEngineering-7B-slerp
- senseable/WestLake-7B-v2
model-index:
- name: BenchmarkEngineering-F2-7B-slerp
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 73.46
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=weezywitasneezy/BenchmarkEngineering-F2-7B-slerp
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 88.88
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=weezywitasneezy/BenchmarkEngineering-F2-7B-slerp
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 64.5
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=weezywitasneezy/BenchmarkEngineering-F2-7B-slerp
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 72.37
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=weezywitasneezy/BenchmarkEngineering-F2-7B-slerp
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 86.11
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=weezywitasneezy/BenchmarkEngineering-F2-7B-slerp
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 69.29
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=weezywitasneezy/BenchmarkEngineering-F2-7B-slerp
      name: Open LLM Leaderboard
---

# BenchmarkEngineering-F2-7B-slerp

This merge seeks to further improve on the original BenchmarkEngineering by integrating the Westlake-7B-v2 model. It does boost the Winogrande score but at the cost of the other benchmarks.

BenchmarkEngineering-F2-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [weezywitasneezy/BenchmarkEngineering-7B-slerp](https://huggingface.co/weezywitasneezy/BenchmarkEngineering-7B-slerp)
* [senseable/WestLake-7B-v2](https://huggingface.co/senseable/WestLake-7B-v2)


# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_weezywitasneezy__BenchmarkEngineering-F2-7B-slerp)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |75.77|
|AI2 Reasoning Challenge (25-Shot)|73.46|
|HellaSwag (10-Shot)              |88.88|
|MMLU (5-Shot)                    |64.50|
|TruthfulQA (0-shot)              |72.37|
|Winogrande (5-shot)              |86.11|
|GSM8k (5-shot)                   |69.29|

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: weezywitasneezy/BenchmarkEngineering-7B-slerp
        layer_range: [0, 32]
      - model: senseable/WestLake-7B-v2
        layer_range: [0, 32]
merge_method: slerp
base_model: weezywitasneezy/BenchmarkEngineering-7B-slerp
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 = "weezywitasneezy/BenchmarkEngineering-F2-7B-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"])
```