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---
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"])
``` |