Instructions to use zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B with Transformers:

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

pipe = pipeline("text-generation", model="zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B")
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-Unleashed-v2.1-70B")
model = AutoModelForCausalLM.from_pretrained("zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B 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-Unleashed-v2.1-70B"
# 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-Unleashed-v2.1-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B

SGLang

How to use zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B 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-Unleashed-v2.1-70B" \
    --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-Unleashed-v2.1-70B",
		"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-Unleashed-v2.1-70B" \
        --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-Unleashed-v2.1-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B with Docker Model Runner:
```
docker model run hf.co/zerofata/L3.3-GeneticLemonade-Unleashed-v2.1-70B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GENETIC LEMONADE

UNLEASHED v2.1

01 // OVERVIEW

An experimental release.

zerofata/GeneticLemonade-Unleashed qlora trained on a test dataset. Performance is improved from the original in my testing, but there are possibly (likely?) areas where the model will underperform which I am looking for feedback on.

This is a creative model intended to excel at character driven RP / ERP. It has not been tested or trained on adventure stories or any large amounts of creative writing.

02 // SILLYTAVERN SETTINGS

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

Recommended Samplers

> Temp: 0.9 - 1.0

> MinP: 0.03 - 0.04

> Dry: 0.8, 1.75, 4

Instruct

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

03 // QUANTIZATIONS

GGUF

> iMatrix (mradermacher)

EXL2

> 4bpw
> 4.5bpw
> 6bpw

04 // DATASET

Model was trained on a tiny synthetic dataset of 640k tokens, approximately 190 conversations. Data was generated by script and then manually reviewed / edited.

The dataset is approximately 60% SFW and 40% NSFW. 90% multi turn RP conversations, 5% creative writing and 5% miscellaneous.

It is an experiment to see how models perform when provided with small amounts of high quality synthetic data, as opposed to human data.