README.md

---
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"])
```