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---
tags:
- merge
- mergekit
- lazymergekit
- ChaoticNeutrals/Eris_Remix_7B
- Virt-io/Erebus-Holodeck-7B
base_model:
- ChaoticNeutrals/Eris_Remix_7B
- Virt-io/Erebus-Holodeck-7B
license: cc-by-nc-4.0
---
# OxytocinErosEngineeringF1-7B-slerp
<img src="https://cdn-uploads.huggingface.co/production/uploads/632b22e66cb20ba0ae82bf06/ei6PcV1sk_qSPo8GgGms-.png"
width="512"
height="512" />
OxytocinErosEngineeringF1-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [ChaoticNeutrals/Eris_Remix_7B](https://huggingface.co/ChaoticNeutrals/Eris_Remix_7B)
* [Virt-io/Erebus-Holodeck-7B](https://huggingface.co/Virt-io/Erebus-Holodeck-7B)
Thanks to MraderMarcher for providing GGUF quants-> [mradermacher/OxytocinErosEngineeringF1-7B-slerp-GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineeringF1-7B-slerp-GGUF)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_weezywitasneezy__OxytocinErosEngineeringF1-7B-slerp)
| Metric |Value|
|---------------------------------|----:|
|Avg. |69.22|
|AI2 Reasoning Challenge (25-Shot)|67.15|
|HellaSwag (10-Shot) |86|
|MMLU (5-Shot) |64.73|
|TruthfulQA (0-shot) |54.54|
|Winogrande (5-shot) |81.14|
|GSM8k (5-shot) |61.79|
## 🧩 Configuration
```yaml
slices:
- sources:
- model: ChaoticNeutrals/Eris_Remix_7B
layer_range: [0, 32]
- model: Virt-io/Erebus-Holodeck-7B
layer_range: [0, 32]
merge_method: slerp
base_model: ChaoticNeutrals/Eris_Remix_7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "weezywitasneezy/OxytocinErosEngineeringF1-7B-slerp"
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"])
``` |