metadata

base_model:
  - cstr/llama3.1-8b-spaetzle-v59
  - cstr/llama3.1-8b-spaetzle-v63
  - cstr/llama3.1-8b-spaetzle-v66
  - cstr/llama3.1-8b-spaetzle-v73
tags:
  - merge
  - mergekit
license: llama3
language:
  - en
  - de
library_name: transformers

llama3.1-8b-spaetzle-v74

llama3.1-8b-spaetzle-v74 is a merge of the following models:

EQ-Bench v2_de: 68.05 169/171, en: 75.27 - which is not the best, but it produces decent answers for some trick questions, and i have a sweet spot for that ;)

🧩 Configuration

models:
  - model: cstr/llama3.1-8b-spaetzle-v59
    parameters:
      weight: 0.3
      density: 0.5
  - model: cstr/llama3.1-8b-spaetzle-v63
    parameters:
      weight: 0.15
      density: 0.5
  - model: cstr/llama3.1-8b-spaetzle-v66
    parameters:
      weight: 0.15
      density: 0.5
  - model: cstr/llama3.1-8b-spaetzle-v73
    parameters:
      weight: 0.4
      density: 0.5
base_model: cstr/llama3.1-8b-spaetzle-v59
merge_method: della_linear
parameters:
  int8_mask: true
  normalize: true
  epsilon: 0.1  
  lambda: 1.0   
  density: 0.7
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "cstr/llama3.1-8b-spaetzle-v74"
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"])