---
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:
* [cstr/llama3.1-8b-spaetzle-v59](https://huggingface.co/cstr/llama3.1-8b-spaetzle-v59)
* [cstr/llama3.1-8b-spaetzle-v63](https://huggingface.co/cstr/llama3.1-8b-spaetzle-v63)
* [cstr/llama3.1-8b-spaetzle-v66](https://huggingface.co/cstr/llama3.1-8b-spaetzle-v66)
* [cstr/llama3.1-8b-spaetzle-v73](https://huggingface.co/cstr/llama3.1-8b-spaetzle-v73)

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

```yamlmodels:
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

```python
!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"])
```