“WithIn Us AI” (LLM's)
Collection
"LLM's & Self Evolving LLM's" designed By (WithIn Us AI) at core.. • 4 items • Updated • 1
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")How to use 11-47/Continuum-0.1B with vLLM:
# 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?"
}
]
}'docker model run hf.co/11-47/Continuum-0.1B
How to use 11-47/Continuum-0.1B with SGLang:
# 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?"
}
]
}'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?"
}
]
}'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 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.
| 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 |
| 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 |
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,
)
Apache 2.0