---
base_model:
- NousResearch/Hermes-3-Llama-3.1-70B
- Fizzarolli/L3.1-70b-glitz-v0.2
- cyberagent/Llama-3.1-70B-Japanese-Instruct-2407
- Sao10K/L3-70B-Euryale-v2.1
tags:
- merge
- mergekit
- lazymergekit
- NousResearch/Hermes-3-Llama-3.1-70B
- Fizzarolli/L3.1-70b-glitz-v0.2
- cyberagent/Llama-3.1-70B-Japanese-Instruct-2407
- Sao10K/L3-70B-Euryale-v2.1
---

# L3.1-70b-Ginny

 

L3.1-70b-Ginny is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [NousResearch/Hermes-3-Llama-3.1-70B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-70B)
* [Fizzarolli/L3.1-70b-glitz-v0.2](https://huggingface.co/Fizzarolli/L3.1-70b-glitz-v0.2)
* [cyberagent/Llama-3.1-70B-Japanese-Instruct-2407](https://huggingface.co/cyberagent/Llama-3.1-70B-Japanese-Instruct-2407)
* [Sao10K/L3-70B-Euryale-v2.1](https://huggingface.co/Sao10K/L3-70B-Euryale-v2.1)

I really liked Glitz and Euryale. Though they can get kinda wish-washy and don't follow structure well enough. 
I used Hermes as a base as it has rather good instruct following but it's way too instruct focused. 
I find myself running into Japanese text too. Which neither 3 models are like superb at, so I used cyberagent's Japanese Instruct to give it a boost.

## 🧩 Configuration

```yaml

models:
  - model: NousResearch/Hermes-3-Llama-3.1-70B
    parameters:
      density: 0.33
      weight: 0.25
  - model: Fizzarolli/L3.1-70b-glitz-v0.2
    parameters:
      density: 0.7
      weight: 0.5
  - model: cyberagent/Llama-3.1-70B-Japanese-Instruct-2407
    parameters:
      density: 0.5
      weight: 0.25
  - model: Sao10K/L3-70B-Euryale-v2.1
    parameters:
      density: 0.7
      weight: 0.5

merge_method: ties
base_model: NousResearch/Hermes-3-Llama-3.1-70B
parameters:
  normalize: true
dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "KaraKaraWitch/L3.1-70b-Ginny"
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"])
```