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Or4cl3-1
/
code-slerp

Merge
mergekit
lazymergekit
microsoft/codebert-base
EleutherAI/gpt-neo-x-20b
openai/codex
bigscience/bloom
google/jurassic-1-jumbo
google/t5-v1_1-large
facebook/bart-large
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code-slerp
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---
tags:
- merge
- mergekit
- lazymergekit
- microsoft/codebert-base
- EleutherAI/gpt-neo-x-20b
- openai/codex
- bigscience/bloom
- google/jurassic-1-jumbo
- google/t5-v1_1-large
- facebook/bart-large
base_model:
- microsoft/codebert-base
- EleutherAI/gpt-neo-x-20b
- openai/codex
- bigscience/bloom
- google/jurassic-1-jumbo
- google/t5-v1_1-large
- facebook/bart-large
---

# code-slerp

code-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base)
* [EleutherAI/gpt-neo-x-20b](https://huggingface.co/EleutherAI/gpt-neo-x-20b)
* [openai/codex](https://huggingface.co/openai/codex)
* [bigscience/bloom](https://huggingface.co/bigscience/bloom)
* [google/jurassic-1-jumbo](https://huggingface.co/google/jurassic-1-jumbo)
* [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large)
* [facebook/bart-large](https://huggingface.co/facebook/bart-large)

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: microsoft/codebert-base
        layer_range: [0, 32]
      - model: EleutherAI/gpt-neo-x-20b
        layer_range: [0, 32]
      - model: openai/codex
        layer_range: [0, 32]
      - model: bigscience/bloom
        layer_range: [0, 32]
      - model: google/jurassic-1-jumbo
        layer_range: [0, 32]
      - model: google/t5-v1_1-large
        layer_range: [0, 32]
      - model: facebook/bart-large
        layer_range: [0, 32]
merge_method: slerp
base_model: microsoft/codebert-base
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/code-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"])
```