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---
tags:
- merge
- mergekit
- lazymergekit
- abideen/AlphaMonarch-dora
base_model:
- abideen/AlphaMonarch-dora
license: cc-by-nc-4.0
language:
- de
- en
---
# Spaetzle-v60-7b
This is a progressive (mostly dare-ties, but also slerp i.a.) merge with the intention of suitable compromise for English and German local tasks.
Spaetzle-v60-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [abideen/AlphaMonarch-dora](https://huggingface.co/abideen/AlphaMonarch-dora)
* [cstr/Spaetzle-v58-7b](https://huggingface.co/cstr/Spaetzle-v58-7b)
## Benchmarks
The performance looks ok so far: e.g. we get in EQ-Bench: Score (v2_de): 65.08 (Parseable: 171.0).
From the [Occiglot Euro LLM Leaderboard](https://huggingface.co/spaces/occiglot/euro-llm-leaderboard):
| Model | DE | EN | ARC EN | TruthfulQA EN | Belebele EN | HellaSwag EN | MMLU EN | ARC DE | TruthfulQA DE | Belebele DE | HellaSwag DE | MMLU DE |
|--------------------------------------------------------|-------|-------|--------|---------------|-------------|--------------|---------|--------|---------------|-------------|--------------|---------|
| mistral-community/Mixtral-8x22B-v0.1 | 66.81 | 72.87 | 70.56 | 52.29 | 93.89 | 70.41 | 77.17 | 63.9 | 29.31 | 92.44 | 77.9 | 70.49 |
| **cstr/Spaetzle-v60-7b** | 60.95 | 71.65 | 69.88 | 66.24 | 90.11 | 68.43 | 63.59 | 58 | 37.31 | 84.22 | 70.09 | 55.11 |
| VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct | 60.07 | 74.71 | 74.49 | 66.19 | 91.67 | 74.55 | 66.65 | 59.37 | 29.57 | 88.56 | 66.43 | 56.44 |
| occiglot/occiglot-7b-de-en-instruct | 56.65 | 61.7 | 60.41 | 49.38 | 81.22 | 60.43 | 57.06 | 54.49 | 31.09 | 77.22 | 68.84 | 51.59 |
| occiglot/occiglot-7b-de-en | 54.01 | 58.78 | 55.63 | 42.33 | 79.11 | 59.99 | 56.84 | 50.56 | 26.27 | 74.33 | 67.42 | 51.46 |
| meta-llama/Meta-Llama-3-8B | 53.89 | 63.08 | 58.02 | 43.87 | 86.44 | 61.75 | 65.3 | 46.45 | 24.24 | 81.11 | 62.48 | 55.18 |
| mistralai/Mistral-7B-Instruct-v0.2 | 53.52 | 67.63 | 63.74 | 66.81 | 82.44 | 65.96 | 59.2 | 48.59 | 37.69 | 68.89 | 62.24 | 50.2 |
| occiglot/occiglot-7b-eu5-instruct | 53.15 | 57.78 | 55.89 | 44.9 | 74.67 | 59.92 | 53.51 | 52.95 | 28.68 | 66.78 | 68.52 | 48.82 |
| clibrain/lince-mistral-7b-it-es | 52.98 | 62.43 | 62.46 | 43.32 | 82.44 | 63.86 | 60.06 | 49.44 | 28.17 | 75 | 61.64 | 50.64 |
| mistralai/Mistral-7B-v0.1 | 52.8 | 62.73 | 61.26 | 42.62 | 84.44 | 62.89 | 62.46 | 47.65 | 28.43 | 73.89 | 61.06 | 52.96 |
| LeoLM/leo-mistral-hessianai-7b | 51.78 | 56.11 | 52.22 | 42.92 | 73.67 | 57.86 | 53.88 | 47.48 | 25.25 | 69.11 | 68.21 | 48.83 |
And for the int4-inc quantized version, from [Low-bit Quantized Open LLM Leaderboard](https://huggingface.co/spaces/Intel/low_bit_open_llm_leaderboard):
| Type | Model | Average ⬆️ | ARC-c | ARC-e | Boolq | HellaSwag | Lambada | MMLU | Openbookqa | Piqa | Truthfulqa | Winogrande | #Params (B) | #Size (G) |
|------|-------------------------------------------|------------|-------|-------|-------|-----------|---------|-------|------------|-------|------------|------------|-------------|-----------|
| 🍒 | Intel/SOLAR-10.7B-Instruct-v1.0-int4-inc | 68.49 | 60.49 | 82.66 | 88.29 | 68.29 | 73.36 | 62.43 | 35.6 | 80.74 | 56.06 | 76.95 | 10.57 | 5.98 |
| 🍒 | **cstr/Spaetzle-v60-7b-int4-inc** | **68.01** | **62.12** | **85.27** | **87.34** | **66.43** | **70.58** | **61.39** | **37** | **82.26** | **50.18** | **77.51** | **7.04** | **4.16** |
| 🔷 | TheBloke/SOLAR-10.7B-Instruct-v1.0-GGUF | 66.6 | 60.41 | 83.38 | 88.29 | 67.73 | 52.42 | 62.04 | 37.2 | 82.32 | 56.3 | 75.93 | 10.73 | 6.07 |
| 🔷 | cstr/Spaetzle-v60-7b-Q4_0-GGUF | 66.44 | 61.35 | 85.19 | 87.98 | 66.54 | 52.78 | 62.05 | 40.6 | 81.72 | 47 | 79.16 | 7.24 | 4.11 |
| 🍒 | Intel/Mistral-7B-Instruct-v0.2-int4-inc | 65.73 | 55.38 | 81.44 | 85.26 | 65.67 | 70.89 | 58.66 | 34.2 | 80.74 | 51.16 | 73.95 | 7.04 | 4.16 |
| 🍒 | Intel/Phi-3-mini-4k-instruct-int4-inc | 65.09 | 57.08 | 83.33 | 86.18 | 59.45 | 68.14 | 66.62 | 38.6 | 79.33 | 38.68 | 73.48 | 3.66 | 2.28 |
| 🔷 | TheBloke/Mistral-7B-Instruct-v0.2-GGUF | 63.52 | 53.5 | 77.9 | 85.44 | 66.9 | 50.11 | 58.45 | 38.8 | 77.58 | 53.12 | 73.4 | 7.24 | 4.11 |
| 🍒 | Intel/Meta-Llama-3-8B-Instruct-int4-inc | 62.93 | 51.88 | 81.1 | 83.21 | 57.09 | 71.32 | 62.41 | 35.2 | 78.62 | 36.35 | 72.14 | 7.2 | 5.4 |
Contamination check results (reference model: Mistral instruct 7b v0.1):
- MMLU: result < 0.1, %: 0.19
- TruthfulQA: result < 0.1, %: 0.34
- GSM8k: result < 0.1, %: 0.39
## 🧩 Configuration
```yaml
models:
- model: cstr/Spaetzle-v58-7b
# no parameters necessary for base model
- model: abideen/AlphaMonarch-dora
parameters:
density: 0.60
weight: 0.30
merge_method: dare_ties
base_model: cstr/Spaetzle-v58-7b
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/Spaetzle-v60-7b"
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"])
``` |