Instructions to use zerofata/L3.3-GeneticLemonade-Final-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zerofata/L3.3-GeneticLemonade-Final-70B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="zerofata/L3.3-GeneticLemonade-Final-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-Final-70B")
model = AutoModelForCausalLM.from_pretrained("zerofata/L3.3-GeneticLemonade-Final-70B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zerofata/L3.3-GeneticLemonade-Final-70B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zerofata/L3.3-GeneticLemonade-Final-70B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zerofata/L3.3-GeneticLemonade-Final-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zerofata/L3.3-GeneticLemonade-Final-70B

SGLang

How to use zerofata/L3.3-GeneticLemonade-Final-70B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "zerofata/L3.3-GeneticLemonade-Final-70B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zerofata/L3.3-GeneticLemonade-Final-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "zerofata/L3.3-GeneticLemonade-Final-70B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zerofata/L3.3-GeneticLemonade-Final-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zerofata/L3.3-GeneticLemonade-Final-70B with Docker Model Runner:
```
docker model run hf.co/zerofata/L3.3-GeneticLemonade-Final-70B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Genetic Lemonade Final

Inspired to learn how to merge by the Nevoria series from SteelSkull.

This model is the second result of the Genetic Lemonade series.

Model Comparison

Designed for RP and creative writing, all three models are focused around striking a balance between writing style, creativity and intelligence. The basic differences between the models are below.

Version	Strength	Weakness
Unleashed	Well balanced	Somewhat censored
Final	Fully uncensored	Least intelligent
Sunset	Well balanced, most intelligent	GPTisms / weakest writing style

SillyTavern Settings

Llam@ception recommended for sane defaults if unsure, import them to SillyTavern and they're plug n play.

Sampler Settings

Temp: 0.9-1.0
MinP: 0.03-0.05
Dry: 0.8, 1.75, 4

Temperature last, neutralize other samplers. This model natively strikes a balance of creativity & intelligence.

Instruct

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

Quants

GGUF

EXL2

Merge Details

Merge Method

This model was merged using the SCE merge method.

The base aims to build a strong general purpose model using high performing models that are trained on various datasets from different languages / cultures. This is to reduce the chance of the same datasets appearing multiple times to build natural creativity into L3.3 The second merge aims to impart specific RP / creative writing knowledge, again focusing on trying to find high performing models that use or likely use different datasets.

Base_6_v2

models:
  - model: OpenBuddy/openbuddy-llama3.3-70b-v24.1-131k
  - model: nbeerbower/llama3.1-kartoffeldes-70B
  - model: tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3
  - model: SicariusSicariiStuff/Negative_LLAMA_70B
select_topk: .15
merge_method: sce
base_model: meta-llama/Llama-3.3-70B-Instruct
out_dtype: bfloat16
dype: float32
tokenizer:
  source: base

Genetic Lemonade Final

models:
  - model: EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1
  - model: LatitudeGames/Wayfarer-Large-70B-Llama-3.3
  - model: crestf411/L3.1-nemotron-sunfall-v0.7.0
  - model: SicariusSicariiStuff/Negative_LLAMA_70B
  - model: Sao10K/L3.3-70B-Euryale-v2.3
merge_method: sce
base_model: ./Base_6_v2
select_topk: 0.15
out_dtype: bfloat16
dype: float32
tokenizer:
  source: union