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---
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"])
``` |