KaraKaraWitch/L3.1-70b-Inori
Inori is the second 70b for the weekend for me to play around.
Learning from the previous model, I yeeted Hermes into the atmosphere and used Glitz as a base.
Inori takes a different approach by using Model Stock.
- Dracarys (I just threw it in, but can be useful for code)
- Euryale (You all know it!)
- Cathallama (Athene + turboderp_cat)
- New Dawn (I heard people like it so I added it in)
- Celeste (RP)
- Japanese-Instruct (Enhancement of Japanese Language for the weebs out there.)
No Hermes was harmed in the making of this model stock merge.
L3.1-70b-Inori is a merge of the following models using LazyMergekit:
- abacusai/Dracarys-Llama-3.1-70B-Instruct
- Sao10K/L3-70B-Euryale-v2.1
- gbueno86/Cathallama-70B
- sophosympatheia/New-Dawn-Llama-3.1-70B-v1.1
- nothingiisreal/L3.1-70B-Celeste-V0.1-BF16
- cyberagent/Llama-3.1-70B-Japanese-Instruct-2407
General Thoughts
- This model has some weird censorship issues. Soketimes it triggers that it can't generate explicit text while sometimes it doesn't.
- For that reason, I don't recommend people to use this model.
Yap / Chat Format
L3 Instruct.
Quants & Hosts
🧩 Configuration
models:
- model: Fizzarolli/L3.1-70b-glitz-v0.2
- model: cyberagent/Llama-3.1-70B-Japanese-Instruct-2407
- model: Sao10K/L3-70B-Euryale-v2.1
- model: nothingiisreal/L3.1-70B-Celeste-V0.1-BF16
- model: sophosympatheia/New-Dawn-Llama-3.1-70B-v1.1
- model: gbueno86/Cathallama-70B
- model: abacusai/Dracarys-Llama-3.1-70B-Instruct
merge_method: model_stock
base_model: Fizzarolli/L3.1-70b-glitz-v0.2
parameters:
normalize: true
dtype: bfloat16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "KaraKaraWitch/L3.1-70b-Inori"
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"])