Benchmarkengineering F2 7B Slerp

🚀 BenchmarkEngineering-F2-7B-slerp

This model aims to enhance the original BenchmarkEngineering by integrating the Westlake-7B-v2 model. While it boosts the Winogrande score, it comes at the cost of performance in other benchmarks. BenchmarkEngineering-F2-7B-slerp is a merged model created using LazyMergekit, combining the following two models:

• weezywitasneezy/BenchmarkEngineering-7B-slerp
• senseable/WestLake-7B-v2

📚 Documentation

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

🧩 Configuration

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 Examples

Basic Usage

!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"])

📄 License

This model is licensed under the CC BY-NC 4.0 license.