README.md

---
license: apache-2.0
base_model:
- allenai/Olmo-3-7B
- allenai/Olmo-3-7B-Instruct
- allenai/Olmo-3-7B-Think
- allenai/Olmo-3-7B-Think-SFT
- allenai/Olmo-3-7B-Think-DPO
- allenai/Olmo-3-7B-RL-Zero-IF
- allenai/Olmo-3-7B-RL-Zero-Math
- allenai/Olmo-3-7B-RL-Zero-Code
- allenai/Olmo-3-7B-RL-Zero-Mix
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- allenai/Olmo-3-7B
- allenai/Olmo-3-7B-Instruct
- allenai/Olmo-3-7B-Think
- allenai/Olmo-3-7B-Think-SFT
- allenai/Olmo-3-7B-Think-DPO
- allenai/Olmo-3-7B-RL-Zero-IF
- allenai/Olmo-3-7B-RL-Zero-Math
- allenai/Olmo-3-7B-RL-Zero-Code
- allenai/Olmo-3-7B-RL-Zero-Mix
---

# olmo3-7b-slerp

olmo3-7b-slerp is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [allenai/Olmo-3-7B](https://huggingface.co/allenai/Olmo-3-7B)
* [allenai/Olmo-3-7B-Instruct](https://huggingface.co/allenai/Olmo-3-7B-Instruct)
* [allenai/Olmo-3-7B-Think](https://huggingface.co/allenai/Olmo-3-7B-Think)
* [allenai/Olmo-3-7B-Think-SFT](https://huggingface.co/allenai/Olmo-3-7B-Think-SFT)
* [allenai/Olmo-3-7B-Think-DPO](https://huggingface.co/allenai/Olmo-3-7B-Think-DPO)
* [allenai/Olmo-3-7B-RL-Zero-IF](https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-IF)
* [allenai/Olmo-3-7B-RL-Zero-Math](https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-Math)
* [allenai/Olmo-3-7B-RL-Zero-Code](https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-Code)
* [allenai/Olmo-3-7B-RL-Zero-Mix](https://huggingface.co/allenai/Olmo-3-7B-RL-Zero-Mix)

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: allenai/Olmo-3-7B
        layer_range: [0, 32]
      - model: allenai/Olmo-3-7B-Instruct
        layer_range: [0, 32]
      - model: allenai/Olmo-3-7B-Think
        layer_range: [0, 32]
      - model: allenai/Olmo-3-7B-Think-SFT
        layer_range: [0, 32]
      - model: allenai/Olmo-3-7B-Think-DPO
        layer_range: [0, 32]
      - model: allenai/Olmo-3-7B-RL-Zero-IF
        layer_range: [0, 32]
      - model: allenai/Olmo-3-7B-RL-Zero-Math
        layer_range: [0, 32]
      - model: allenai/Olmo-3-7B-RL-Zero-Code
        layer_range: [0, 32]
      - model: allenai/Olmo-3-7B-RL-Zero-Mix
base_model: allenai/Olmo-3-7B
experts:
  - source_model: allenai/Olmo-3-7B
    weight: 0.2
  - source_model: allenai/Olmo-3-7B-Instruct
    weight: 0.1
  - source_model: allenai/Olmo-3-7B-Think
    weight: 0.1
  - source_model: allenai/Olmo-3-7B-Think-SFT
    weight: 0.1
  - source_model: allenai/Olmo-3-7B-Think-DPO
    weight: 0.1
  - source_model: allenai/Olmo-3-7B-RL-Zero-IF
    weight: 0.1
  - source_model: allenai/Olmo-3-7B-RL-Zero-Math
    weight: 0.1
  - source_model: allenai/Olmo-3-7B-RL-Zero-Code
    weight: 0.1
  - source_model: allenai/Olmo-3-7B-RL-Zero-Mix
    weight: 0.1
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
merge_type: slerp
dtype: bfloat16
layer_range: [0, 32]
```

## 💻 Usage

```python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "jsuheb/olmo3-7b-slerp"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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"])
```