---
base_model:
- crestf411/nemo-sunfall-v0.6.1
- unsloth/Mistral-Nemo-Instruct-2407
- RozGrov/NemoDori-v0.1-12B-MS
tags:
- merge
- mergekit
- lazymergekit
- crestf411/nemo-sunfall-v0.6.1
- unsloth/Mistral-Nemo-Instruct-2407
- RozGrov/NemoDori-v0.1-12B-MS
library_name: transformers
pipeline_tag: text-generation
---

# NemoDori-v0.2-12B-MN-BT

NemoDori-v0.2-12B-MN-BT is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [crestf411/nemo-sunfall-v0.6.1](https://huggingface.co/crestf411/nemo-sunfall-v0.6.1)
* [unsloth/Mistral-Nemo-Instruct-2407](https://huggingface.co/unsloth/Mistral-Nemo-Instruct-2407)
* [RozGrov/NemoDori-v0.1-12B-MS](https://huggingface.co/RozGrov/NemoDori-v0.1-12B-MS)

Experimental 'sequel' of NemoDori ([v0.1](https://huggingface.co/RozGrov/NemoDori-v0.1-12B-MS)), an ERP-focused model, just for testing purpose. I still don't know what I've done...

My short experience using this:
- When you instruct it to roleplay, it generates short chat-like response and to-the-point. (sometimes really short)
- It advances the story slowly (even slower than v0.1 i think), responding to the last roleplay message quite nicely.
- Creativity is *maybe* good(?).
- Can follow instructions quite well (even on depth-0).

<u>Update 1</u>:
<br>
I was playing around with [**Talemate**](https://github.com/vegu-ai/talemate) and... this model is pretty good. It was able to follow Talemate's instructions well, which Talemate then parsed it into it's format.
<br>
It can become all of Talemate agents (mostly maybe). So far i've tested, it is best when having a Conversation, capable of generating character's attributes (Creator) and the world state. When it's used for narrating (Narrator), sometimes it speaks for you and includes the Talemate's conversation (for this one, maybe I just didn't have a good instruction for it).
<br><br>
Those are my experiences using default presets from Talemate. I did tweaked them and played a bit near the end, and it does affect the results in a good way. 
<br>
I'll try to test it again some more later.

## 🧩 Configuration

```yaml
models:
  - model: crestf411/nemo-sunfall-v0.6.1
    parameters:
      weight: 0.5
  - model: unsloth/Mistral-Nemo-Instruct-2407
    parameters:
      weight: 0.3
  - model: RozGrov/NemoDori-v0.1-12B-MS
    parameters:
      weight: 1.0
merge_method: breadcrumbs_ties
base_model: RozGrov/NemoDori-v0.1-12B-MS
parameters:
    density: 0.95
    gamma: 0.01
dtype: float16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "RozGrov/NemoDori-v0.2-12B-MN-BT"
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"])
```