How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="zerofata/L3.3-GeneticLemonade-Opus-70B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zerofata/L3.3-GeneticLemonade-Opus-70B")
model = AutoModelForCausalLM.from_pretrained("zerofata/L3.3-GeneticLemonade-Opus-70B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
Quick Links

GENETIC LEMONADE

Opus

image/png

01 // OVERVIEW

Felt like making a merge.

This model combines three individually solid, stable and distinctly different RP models.

zerofata/GeneticLemonade-Unleashed-v3 Creative, generalist RP / ERP model.

Delta-Vector/Plesio-70B Unique prose and unique dialogue RP / ERP model.

TheDrummer/Anubis-70B-v1.1 Character portrayal, neutrally aligned RP / ERP model.

02 // SILLYTAVERN SETTINGS

Play with these, they are not the 'best' settings just a stable baseline.

Recommended Samplers

> Temp: 0.9 - 1.2
> MinP: 0.03 - 0.04
> TopP: 0.9 - 1.0
> Dry: 0.8, 1.75, 4

Instruct

Llama-3-Instruct-Names but you will need to uncheck "System same as user".

03 // QUANTIZATIONS

04 // MERGE CONFIG

models:
  - model: zerofata/L3.3-GeneticLemonade-Unleashed-v3-70B
  - model: Delta-Vector/Plesio-70B
  - model: TheDrummer/Anubis-70B-v1.1
base_model: shisa-ai/shisa-v2-llama3.3-70b
merge_method: sce
parameters:
  select_topk: 0.16
out_dtype: bfloat16
tokenizer:
 source: base
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