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---
tags:
- merge
- mergekit
- lazymergekit
- nbeerbower/llama-3-wissenschaft-8B-v2
base_model:
- nbeerbower/llama-3-wissenschaft-8B-v2
license: llama3
language:
- en
- de
---
# llama3-8b-spaetzle-v20
llama3-8b-spaetzle-v20 is a merge of the following models:
* [cstr/llama3-8b-spaetzle-v13](https://huggingface.co/cstr/llama3-8b-spaetzle-v13)
* [nbeerbower/llama-3-wissenschaft-8B-v2](https://huggingface.co/nbeerbower/llama-3-wissenschaft-8B-v2)
# Benchmarks
On EQ-Bench v2_de it achieves 65.7 (171/171 parseable). From Open LLM Leaderboard ([details](https://huggingface.co/datasets/open-llm-leaderboard/details_cstr__llama3-8b-spaetzle-v20/blob/main/results_2024-05-25T12-52-23.640126.json)):
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|----------------------------------|------------|-------|-----------|-------|------------|------------|-------|
| cstr/llama3-8b-spaetzle-v20 | 71.83 | 70.39 | 85.69 | 68.52 | 60.98 | 78.37 | 67.02 |
## 🧩 Configuration
```yaml
models:
- model: cstr/llama3-8b-spaetzle-v13
# no parameters necessary for base model
- model: nbeerbower/llama-3-wissenschaft-8B-v2
parameters:
density: 0.65
weight: 0.4
merge_method: dare_ties
base_model: cstr/llama3-8b-spaetzle-v13
parameters:
int8_mask: true
dtype: bfloat16
random_seed: 0
tokenizer_source: base
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "cstr/llama3-8b-spaetzle-v20"
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"])
``` |