Instructions to use KissTheHabit/IDA_MoE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KissTheHabit/IDA_MoE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="KissTheHabit/IDA_MoE")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("KissTheHabit/IDA_MoE", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use KissTheHabit/IDA_MoE with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KissTheHabit/IDA_MoE" # 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_MoE", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/KissTheHabit/IDA_MoE
- SGLang
How to use KissTheHabit/IDA_MoE 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_MoE" \ --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_MoE", "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_MoE" \ --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_MoE", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use KissTheHabit/IDA_MoE with Docker Model Runner:
docker model run hf.co/KissTheHabit/IDA_MoE
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="KissTheHabit/IDA_MoE")# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("KissTheHabit/IDA_MoE", dtype="auto")IDA MoE Council
KissTheHabit/IDA_MoE is the H100-targeted escalation-reserve artifact repository for the IDA family.
It uses the native IDA Lattice causal language model architecture with a shared trunk and an eleven-member personality council.
This is not a generic sparse MoE trained to collapse all experts into interchangeable compute paths. The council is designed to preserve differentiated internal claimants while routing a bounded subset into active participation.
Architecture
- Model family:
ida_lattice - Model class:
IDALatticeForCausalLM - Task: causal language modeling and text generation
- Deployment role: high-pressure escalation and contradiction review
- Approximate model scale:
~2.7Bparameters per student body - Shared tokenizer:
KissTheHabit/ida_lattice_bpe_32k
Shared Trunk
| Attribute | Value |
|---|---|
| Vocabulary size | 32,000 |
| Hidden size | 4,096 |
| Layers | 8 |
| Attention heads | 8 |
| Intermediate size | 16,384 |
| Context length | 2,048 |
| Recurrent state size | 1,024 |
| Local attention window | 256 |
| Workspace | 8 × 512 |
| Student state size | 512 |
| Future prediction horizon | 2 |
| Thalamic route count | 6 |
| Action gate size | 6 |
Personality Council
- Cognitive pressure routes:
9 - Named personality experts:
11 - Personality residual expert width:
4,096 - Active experts during standard training:
top_k = 2 - Serious runtime escalation target:
top_k = 3 - High-contradiction review target:
top_k = 4 - Explicit full review: all
11
The cognitive routes are pressure signals, not personalities:
PERCEPTIONMEMORYSALIENCECAUSAL_INSPECTIONPLANNINGINHIBITIONCREATIONERROR_CORRECTIONEXPRESSION
The personality experts are the enduring IDA family seats:
IDAJUDGESENTINELPRISMECHOATLASVECTORFORGESHADEPULSEORBIT
Repository Layout
Artifacts are stored by student and developmental version:
students/{STUDENT}/{version}
# Gated model: Login with a HF token with gated access permission hf auth login