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

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

NemoDori-v0.2-12B-MN-BT is a merge of the following models using LazyMergekit:

• crestf411/nemo-sunfall-v0.6.1
• unsloth/Mistral-Nemo-Instruct-2407
• RozGrov/NemoDori-v0.1-12B-MS

Experimental 'sequel' of NemoDori (v0.1), 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 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

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

!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"])