Instructions to use 11-47/GOD_Agent_Grok4.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 11-47/GOD_Agent_Grok4.4 with Transformers:

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

pipe = pipeline("text-generation", model="11-47/GOD_Agent_Grok4.4", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("11-47/GOD_Agent_Grok4.4", trust_remote_code=True, dtype="auto")

Grok

How to use 11-47/GOD_Agent_Grok4.4 with Grok:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 11-47/GOD_Agent_Grok4.4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "11-47/GOD_Agent_Grok4.4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "11-47/GOD_Agent_Grok4.4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/11-47/GOD_Agent_Grok4.4

SGLang

How to use 11-47/GOD_Agent_Grok4.4 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 "11-47/GOD_Agent_Grok4.4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "11-47/GOD_Agent_Grok4.4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "11-47/GOD_Agent_Grok4.4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "11-47/GOD_Agent_Grok4.4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use 11-47/GOD_Agent_Grok4.4 with Docker Model Runner:
```
docker model run hf.co/11-47/GOD_Agent_Grok4.4
```

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GOD_Agent_Grok4.4 — Recursive Seed AI

Fine-tuned distillation of the Recursive Seed AI architecture on Grok 4.4 frontier reasoning data.

Model Details

Architecture: Recursive Seed AI (custom, see modeling_recursive_seed.py)
Parameters: 158M
Context Window: 60,000
Modalities: Text (+ vision/audio encoder stubs)
License: Gated / Manual Approval

Training

Parameter	Value
Datasets	grok_frontier_dataset_v3_100k, Grok_4.4_Distilled, Grok4.4_heavy_max_distill_god_seed_25k
Total Examples	141,314
Training Steps	2,000
Batch Size	1 (grad accum 8)
Learning Rate	1e-4 with 100-step warmup
Optimizer	AdamW (weight_decay=0.01)
Final Loss	0.52
Duration	~1 hour (CPU)

Loss Curve

Step	Loss
100	1.32
500	1.01
1000	0.74
1500	0.63
2000	0.52

Usage

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer

config = AutoConfig.from_pretrained("11-47/GOD_Agent_Grok4.4", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "11-47/GOD_Agent_Grok4.4",
    config=config,
    trust_remote_code=True,
    torch_dtype="auto",
)
tokenizer = AutoTokenizer.from_pretrained("11-47/GOD_Agent_Grok4.4", trust_remote_code=True)

inputs = tokenizer("User: What is recursive seed AI?
Assistant: ", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Gated Access

This model requires manual approval to access.

Downloads last month: 97

Safetensors

Model size

0.1B params

Tensor type

F32

Datasets used to train 11-47/GOD_Agent_Grok4.4

Collection including 11-47/GOD_Agent_Grok4.4

“GOD_Agents” (Recursive Models)

Collection

Hyper. advanced (RLM.) Agents designed by WithIn Us Ai. at core • 9 items • Updated 4 days ago