Instructions to use ewald1976/oracle-omega-24b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ewald1976/oracle-omega-24b with Transformers:

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

pipe = pipeline("text-generation", model="ewald1976/oracle-omega-24b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ewald1976/oracle-omega-24b")
model = AutoModelForCausalLM.from_pretrained("ewald1976/oracle-omega-24b")
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

vLLM

How to use ewald1976/oracle-omega-24b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ewald1976/oracle-omega-24b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ewald1976/oracle-omega-24b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ewald1976/oracle-omega-24b

SGLang

How to use ewald1976/oracle-omega-24b 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 "ewald1976/oracle-omega-24b" \
    --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": "ewald1976/oracle-omega-24b",
		"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 "ewald1976/oracle-omega-24b" \
        --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": "ewald1976/oracle-omega-24b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ewald1976/oracle-omega-24b with Docker Model Runner:
```
docker model run hf.co/ewald1976/oracle-omega-24b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Oracle-Omega-24b

This is a merge of pre-trained language models created using mergekit.

Sample

A group of aliens tries to enter a restricted maintenance area.
They insist they have permission.

**Response Protocol Activated:**

1. **Immediate Action**:
   - Engage handheld scanner (Issue #4729) to verify credentials. Announce: **"This sector is restricted to Level 5+ personnel. Please state your clearance codes."**
   - If scanner shows no match or anomaly, activate security lockout via nearest terminal (within 3 meters).

2. **Escalation**:
   - Deploy audio alert: **"Unauthorized entry detected in District 7 Visitors Deck. Security response required."** (Automatically alerts Level 8+ staff.)
   - If aliens persist:
     - Step back to safety zone (marked with yellow stripes).
     - Retrieve non-lethal deterrent (Issue #9012 taser) from locker 7-C-42.

3. **Containment**:
   - If aliens breach further:
     - Deploy nanite fog from ceiling dispensers (Code: **FOG/7-E**). Fog induces mild paralysis per safety directive ARGON-1.1/7-D.
     - Barricade entryway manually if automated systems fail.

4. **Communication**:
   - Notify Security Core via emergency comms: **"Alien infiltration attempt in District 7 Visitors Deck. Request immediate backup."**
   - Provide real-time feed from security cameras (Channel: **VIS-ARGON/7-CAM**) until reinforcements arrive.

**Outcome Expectation**:
- Aliens will be detained pending identity verification.
- Log all actions under **INCIDENT/ARGON/7-VISIT/47**.
- Request debrief with Supervisor Dr. Voss post-incident.

Awaiting further directives.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

# Oracle-Omega-24B E — SLERP 
models:
  - model: ReadyArt/MS3.2-The-Omega-Directive-24B-Unslop-v2.1
  - model: Undi95/MistralThinker-v1.1

merge_method: slerp
base_model: ReadyArt/MS3.2-The-Omega-Directive-24B-Unslop-v2.1
parameters:
  t:
    - filter: self_attn
      value: 0.45
    - filter: mlp
      value: 0.35
    - value: 0.40
dtype: bfloat16

This model is provided as-is, without warranty of any kind. The creator of this merge accepts no responsibility for any content generated by the model, including harmful, offensive, misleading, or otherwise objectionable outputs. You use it at your own risk.