NemoDori-v0.2-12B-MN-BT
NemoDori-v0.2-12B-MN-BT is a merge of the following models using LazyMergekit:
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).
Update 1: 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. 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). 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. 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"])