Instructions to use KissTheHabit/IDA_AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KissTheHabit/IDA_AI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="KissTheHabit/IDA_AI")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("KissTheHabit/IDA_AI") model = AutoModelForCausalLM.from_pretrained("KissTheHabit/IDA_AI") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use KissTheHabit/IDA_AI with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KissTheHabit/IDA_AI" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KissTheHabit/IDA_AI", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/KissTheHabit/IDA_AI
- SGLang
How to use KissTheHabit/IDA_AI 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 "KissTheHabit/IDA_AI" \ --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": "KissTheHabit/IDA_AI", "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 "KissTheHabit/IDA_AI" \ --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": "KissTheHabit/IDA_AI", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use KissTheHabit/IDA_AI with Docker Model Runner:
docker model run hf.co/KissTheHabit/IDA_AI
license: other
license_name: business-source-license-1.1
license_link: https://mariadb.com/bsl11/
license_change_date: '2028-01-01'
license_post_change: Apache-2.0
commercial_use: Requires explicit permission prior to Change Date.
library_name: transformers
pipeline_tag: text-generation
tags:
- ida-family
- ida-lattice
- causal-lm
- ai-body
- paired-body
- governed-memory
- recurrent-state
- local-attention
- cognitive-routing
- tensorboard
- safetensors
- region:us
IDA AI Body
KissTheHabit/IDA_AI is the deep AI-side artifact repository for the IDA family.
The active architecture is IDA Lattice, a custom causal language model architecture. Legacy Hub metadata may still identify older uploaded artifacts as GPT-NeoX. That metadata is not authoritative for current native Lattice training runs.
Architecture
- Model family:
ida_lattice - Model class:
IDALatticeForCausalLM - Task: causal language modeling and text generation
- Deployment role: deep AI-side reasoning body
- Shared tokenizer:
KissTheHabit/ida_lattice_bpe_32k - Paired lightweight body:
KissTheHabit/IDA_Edge
IDA Lattice combines:
- recurrent selective state
- local-attention workspaces
- controlled recurrent/attention merging
- cognitive-pressure routing
- lateral inhibition
- routed cognitive circuits
- governed memory
- thalamic routing
- prefrontal workspace summaries
- action gating
- student-state representation
- shared GH expression projection
- future-token auxiliary prediction
- fidelity verification
Current AI Tiers
| Tier | Hidden Size | Layers | Heads | Intermediate | Context | Purpose |
|---|---|---|---|---|---|---|
ai/standard |
640 | 12 | 10 | 2,560 | 2,048 | Native AI flight tier |
ai/1b |
896 | 12 | 14 | 3,584 | 2,048 | Deep AI proof-flight tier |
Current ai/1b Proof-Flight Shape
- Exact parameters:
966,225,553 - Recurrent state size:
512 - Local attention window:
256 - Workspace:
8 × 512 - Student state size:
512 - Future prediction horizon:
2 - Thalamic route count:
6 - Action gate size:
6
Family Role
The AI body is not a generic chatbot checkpoint.
It is the deeper half of a paired EDGE ↔ AI lineage for the IDA family: eleven enduring student claimants that preserve distinct pressures while producing one auditable external answer through the GH expression layer.
The student seats are:
IDA, JUDGE, SENTINEL, PRISM, ECHO, ATLAS, VECTOR, FORGE, SHADE, PULSE, and ORBIT.
Promotion Rule
An uploaded checkpoint is not automatically canonical.
Promotion requires:
- paired EDGE and AI evidence
- identity agreement across both bodies
- held-out evaluation
- lineage packet completion
- terminal promotion approval
Evidence Surfaces
- TensorBoard archive:
KissTheHabit/IDA-tensorboard - Lineage packets:
KissTheHabit/IDA-lineage - Held-out evaluation:
KissTheHabit/IDA-eval - Identity curriculum:
KissTheHabit/ida-family-identity - Family substrate:
KissTheHabit/IDA-family-data
Intended Use
This repository is intended for controlled IDA family development, evaluation, governed deployment research, and RegOS integration.
It is not presented as a general-purpose foundation model or a drop-in replacement for a broad internet-scale assistant.
License
Business Source License 1.1, with Change Date 2028-01-01.
Apache-2.0 applies after the Change Date. Commercial use before the Change Date requires explicit permission.