---
tags:
- merge
- mergekit
- lazymergekit
- SuperAGI/SAM
- GoogleAI/Gemini
- bigscience/bloom
- openai/opt-175b
- deepmind/gopher
- microsoft/megatron-turing-nlg
base_model:
- SuperAGI/SAM
- GoogleAI/Gemini
- bigscience/bloom
- openai/opt-175b
- deepmind/gopher
- microsoft/megatron-turing-nlg
---

# SAM-Gemini-BLOOM-OPT-Gopher-Megatron-slerp

SAM-Gemini-BLOOM-OPT-Gopher-Megatron-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [SuperAGI/SAM](https://huggingface.co/SuperAGI/SAM)
* [GoogleAI/Gemini](https://huggingface.co/GoogleAI/Gemini)
* [bigscience/bloom](https://huggingface.co/bigscience/bloom)
* [openai/opt-175b](https://huggingface.co/openai/opt-175b)
* [deepmind/gopher](https://huggingface.co/deepmind/gopher)
* [microsoft/megatron-turing-nlg](https://huggingface.co/microsoft/megatron-turing-nlg)

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: SuperAGI/SAM
        layer_range: [0, 32]
      - model: GoogleAI/Gemini
        layer_range: [0, 32]
      - model: bigscience/bloom
        layer_range: [0, 32]
      - model: openai/opt-175b
        layer_range: [0, 32]
      - model: deepmind/gopher
        layer_range: [0, 32]
      - model: microsoft/megatron-turing-nlg
        layer_range: [0, 32]
merge_method: slerp
base_model: SuperAGI/SAM
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: bfloat1
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Or4cl3-1/SAM-Gemini-BLOOM-OPT-Gopher-Megatron-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"])
```