README.md

---
tags:
- merge
- mergekit
- lazymergekit
- mlabonne/OrpoLlama-3-8B
- DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental
base_model:
- mlabonne/OrpoLlama-3-8B
- DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental
---

# llama3-discolm-orpo-t2

llama3-discolm-orpo-t2 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B)
* [DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental](https://huggingface.co/DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental)

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: mlabonne/OrpoLlama-3-8B
        layer_range: [0, 32]
      - model: DiscoResearch/Llama3_DiscoLM_German_8b_v0.1_experimental
        layer_range: [1, 32]
merge_method: slerp
base_model: mlabonne/OrpoLlama-3-8B
parameters:
    value: [0, 0.5, 0.7, 0.9]
dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "cstr/llama3-discolm-orpo-t2"
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"])
```