Instructions to use SuperbEmphasis/The-Omega-Directive-12B-EVISCERATED-FT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SuperbEmphasis/The-Omega-Directive-12B-EVISCERATED-FT with Transformers:

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

pipe = pipeline("text-generation", model="SuperbEmphasis/The-Omega-Directive-12B-EVISCERATED-FT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SuperbEmphasis/The-Omega-Directive-12B-EVISCERATED-FT")
model = AutoModelForCausalLM.from_pretrained("SuperbEmphasis/The-Omega-Directive-12B-EVISCERATED-FT", 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 SuperbEmphasis/The-Omega-Directive-12B-EVISCERATED-FT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SuperbEmphasis/The-Omega-Directive-12B-EVISCERATED-FT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SuperbEmphasis/The-Omega-Directive-12B-EVISCERATED-FT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SuperbEmphasis/The-Omega-Directive-12B-EVISCERATED-FT

SGLang

How to use SuperbEmphasis/The-Omega-Directive-12B-EVISCERATED-FT 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 "SuperbEmphasis/The-Omega-Directive-12B-EVISCERATED-FT" \
    --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": "SuperbEmphasis/The-Omega-Directive-12B-EVISCERATED-FT",
		"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 "SuperbEmphasis/The-Omega-Directive-12B-EVISCERATED-FT" \
        --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": "SuperbEmphasis/The-Omega-Directive-12B-EVISCERATED-FT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use SuperbEmphasis/The-Omega-Directive-12B-EVISCERATED-FT with Docker Model Runner:
```
docker model run hf.co/SuperbEmphasis/The-Omega-Directive-12B-EVISCERATED-FT
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

omg it almost works!

I stripped out the 5 least used layers. and then I used SFT over 4 epochs and a high learning rate.... and its almost good!

My goal is to make a new Velvet eclipse with these "less used" paramets stripped out. reducing the size significantly to allow for a higher inference speed, and more room for context.

NOTES

        per_device_train_batch_size = 10,
        gradient_accumulation_steps = 4,
        num_train_epochs = 4, # Set this for 1 full training run.
        learning_rate = 5e-4, # Reduce to 2e-5 for long training runs