Instructions to use 11-47/Continuum-0.1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 11-47/Continuum-0.1B with Transformers:

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

pipe = pipeline("text-generation", model="11-47/Continuum-0.1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("11-47/Continuum-0.1B", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 11-47/Continuum-0.1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "11-47/Continuum-0.1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "11-47/Continuum-0.1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/11-47/Continuum-0.1B

SGLang

How to use 11-47/Continuum-0.1B 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/Continuum-0.1B" \
    --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": "11-47/Continuum-0.1B",
		"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 "11-47/Continuum-0.1B" \
        --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": "11-47/Continuum-0.1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 11-47/Continuum-0.1B with Docker Model Runner:
```
docker model run hf.co/11-47/Continuum-0.1B
```

Continuum-0.1B / README.md

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	---
	license: apache-2.0
	language:
	- en
	tags:
	- continuum
	- hybrid-mind
	- self-evolving
	- causal-lm
	- meta-learning
	- continual-learning
	- emotional-intelligence
	- multimodal
	- memory
	- reinforcement-learning
	model-index:
	- name: Continuum-0.1B
	results: []
	pipeline_tag: text-generation
	library_name: transformers
	---

	# Continuum-0.1B — The Hybrid Mind

	Continuum-0.1B is a ~111M-parameter self-evolving Small Language Model built from
	scratch by 11-47 / WithInUsAI. It unifies 25 autonomous Hybrid-Mind subsystems
	into a single decoder-only transformer forward pass.

	## Architecture

	\| Component \| Spec \|
	\|---\|---\|
	\| Type \| Decoder-only transformer \|
	\| Parameters \| ~111M (tied embeddings) \|
	\| Context \| 64 096 tokens (64K + seed) \|
	\| Layers \| 12 \|
	\| Hidden size \| 768 \|
	\| FFN \| SwiGLU, intermediate=2048 \|
	\| Attention \| GQA (12Q / 4KV heads) \|
	\| Position \| NTK-aware RoPE θ=500 000 \|
	\| Norm \| Pre-norm RMSNorm \|
	\| Dtype \| BFloat16 \|

	## Training

	- Hardware: 2x T4 GPUs (Kaggle) via DataParallel
	- Steps: 3000 (effective batch 16 × 2048 tokens)
	- Resume: Auto-resumes from HF Hub checkpoint if kernel restarts

	## Datasets

	\| Dataset \| Source \|
	\|---------\|--------\|
	\| Claude Opus Mythos 5K \| `WithinUsAI/claude_opus_mythos_5k` \|
	\| Claude Opus 4.8 Distill \| `WithinUsAI/claude_opus_4.8_distill` \|
	\| Claude Mythos Distill \| `WithinUsAI/claude_mythos_distill` \|
	\| Opus 4.7 Thinking Max Distill (25K) \| `WithinUsAI/Opus4.7_thinking_max_distill_god_seed_25k` \|
	\| Claude Opus 4.7 Distilled \| `WithinUsAI/claude_Opus_4.7_Distilled` \|
	\| Mythos Preview 5K v2 \| `11-47/cluade_mythos_preview_5k_v2` \|
	\| Claude Opus 4.8 Max Thinking 5K v2 \| `11-47/claude_opus_4.8_max_thinking_5k_v2` \|

	## Quick Start

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	tokenizer = AutoTokenizer.from_pretrained("11-47/Continuum-0.1B", trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(
	"11-47/Continuum-0.1B",
	torch_dtype=torch.bfloat16,
	device_map="auto",
	trust_remote_code=True,
	)
	```

	## License

	Apache 2.0