MODEL_TEST_CHECKLIST.md

Ouroboros Model Architecture Test Checklist

Supported ModelTypes (from brain.py)

ModelType	Interface	Test Method	Status
LLM	generate()	Text completion/chat	✅
EMBEDDING	encode()	Semantic vectors	✅
RL	act() / step()	Action selection	⬜ (needs SB3 loader)
VISION	detect()	Object detection	⬜
PLANNER	plan()	Trajectory generation	⬜
SLAM	update() + get_pose()	Localization	⬜
CONTROL	control()	Motor commands	⬜
SENSOR	fuse()	Sensor fusion	⬜
COMMS	send() / recv()	Message passing	⬜
STATE_MACHINE	tick()	FSM transitions	⬜
INFINITY_EMBED	infinity-emb async	High-throughput embed	⬜
INFINITY_RERANK	infinity-emb async	Document reranking	✅
INFINITY_CLASSIFY	infinity-emb async	Zero-shot classify	⬜
INFINITY_CLIP	infinity-emb async	Image embedding	⬜
INFINITY_CLAP	infinity-emb async	Audio embedding	⬜
GENERIC	callable fallback	Direct invocation	✅

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Available Pre-Downloaded Models (D:\huggingface\hub)

🔤 Text Generation (LLM)

Model	Size	Test Command
HuggingFaceTB/SmolLM2-1.7B-Instruct	1.7B	plug_model("HuggingFaceTB/SmolLM2-1.7B-Instruct"); generate("Hello")
HuggingFaceTB/SmolLM2-135M-Instruct	135M	plug_model("HuggingFaceTB/SmolLM2-135M-Instruct"); generate("Hello")
LiquidAI/LFM2.5-1.2B-Thinking	1.2B	Thinking/reasoning model
Qwen/Qwen2.5-0.5B-Instruct	0.5B	generate("Explain quantum computing")
Qwen/Qwen2.5-1.5B-Instruct	1.5B	generate("Write a haiku")
TinyLlama/TinyLlama-1.1B-Chat-v1.0	1.1B	Chat completion
google/flan-t5-small	77M	Seq2seq generation
google/gemma-3-1b-it	1B	Instruction-tuned
google/gemma-3-270m	270M	Small Gemma
google/gemma-3-270m-it	270M	Instruction-tuned
gpt2 / openai-community/gpt2	124M	Causal LM baseline
openai-community/gpt2-medium	355M	Medium GPT-2
ibm-granite/granite-4.0-micro	micro	IBM Granite
unsloth/granite-4.0-350m	350M	Unsloth optimized
tiiuae/falcon-rw-1b	1B	Falcon base
aloobun/falcon-1b-cot-t2	1B	Chain-of-thought
microsoft/phi-2	2.7B	Phi-2 reasoning
mistralai/Mistral-7B-Instruct-v0.3	7B	Mistral instruct
meta-llama/Llama-3.2-1B	1B	Llama 3.2
sshleifer/tiny-gpt2	tiny	Unit test model

🧮 Embedding Models

Model	Dims	Test Command
BAAI/bge-small-en-v1.5	384	plug_model("BAAI/bge-small-en-v1.5"); embed_text("test")
BAAI/bge-base-en-v1.5	768	Larger BGE
BAAI/bge-large-en-v1.5	1024	Largest BGE
sentence-transformers/all-MiniLM-L6-v2	384	Fast SBERT
sentence-transformers/all-MiniLM-L12-v2	384	Deeper MiniLM
sentence-transformers/all-mpnet-base-v2	768	MPNet base
sentence-transformers/distiluse-base-multilingual-cased	512	Multilingual
intfloat/e5-small-v2	384	E5 small
intfloat/e5-base-v2	768	E5 base
thenlper/gte-small	384	GTE small
thenlper/gte-large	1024	GTE large
TaylorAI/gte-tiny	384	Tiny GTE
nomic-ai/nomic-embed-text-v1.5	768	Nomic embed
jinaai/jina-embeddings-v2-small-en	512	Jina v2
jinaai/jina-embeddings-v3	1024	Jina v3
Snowflake/snowflake-arctic-embed-xs	384	Arctic embed
ibm-granite/granite-embedding-small-english-r2	384	Granite embed
google/embeddinggemma-300m	varies	Gemma embed
Qwen/Qwen3-Embedding-0.6B	1024	Qwen embed

🔁 Reranking Models

Model	Test Command
BAAI/bge-reranker-base	rerank(query, documents)

🖼️ Vision-Language Models (VLM)

Model	Size	Capabilities
Qwen/Qwen2-VL-2B-Instruct	2B	Vision + text
Qwen/Qwen2.5-VL-3B-Instruct	3B	Improved VL
Qwen/Qwen2.5-VL-7B-Instruct	7B	Large VL
vikhyatk/moondream2	1.6B	Tiny VLM
moondream/starmie-v1	varies	Moondream variant
h2oai/h2ovl-mississippi-800m	800M	H2O VL
TIGER-Lab/VLM2Vec-Qwen2VL-2B	2B	VLM to vectors
owl10/ReCogDrive-VLM-2B	2B	Driving VLM

🎮 Reinforcement Learning (RL)

Model	Environment	Test Command
sb3/ppo-CartPole-v1	CartPole	model.act(obs)
sb3/ppo-LunarLander-v2	LunarLander	model.act(obs)
sb3/ppo-Acrobot-v1	Acrobot	model.act(obs)
sb3/ppo-BreakoutNoFrameskip-v4	Breakout	Atari
sb3/ppo-PongNoFrameskip-v4	Pong	Atari
sb3/ppo-SpaceInvadersNoFrameskip-v4	Space Invaders	Atari
sb3/ppo-MsPacmanNoFrameskip-v4	Ms. Pacman	Atari
sb3/ppo-QbertNoFrameskip-v4	Q*bert	Atari
sb3/ppo-BeamRiderNoFrameskip-v4	Beam Rider	Atari
sb3/ppo-EnduroNoFrameskip-v4	Enduro	Atari
sb3/ppo-AsteroidsNoFrameskip-v4	Asteroids	Atari
sb3/ppo-RoadRunnerNoFrameskip-v4	Road Runner	Atari
sb3/ppo-SeaquestNoFrameskip-v4	Seaquest	Atari
sb3/ppo-MiniGrid-*	MiniGrid	Grid world
sb3/dqn-*	Various	DQN variants
sb3/qrdqn-*	Various	QR-DQN variants
qgallouedec/ppo-MiniGrid-FourRooms-v0	MiniGrid	Four rooms

♟️ Game AI

Model	Game	Test
Maxlegrec/ChessBot	Chess	model.act(board_state)
notjing/chessai	Chess	Chess AI

💻 Code Models

Model	Task	Test
Salesforce/codet5-small	Code generation	generate("def fibonacci")
microsoft/codebert-base	Code understanding	Embeddings

🎨 Image Generation

Model	Type	Test
CompVis/stable-diffusion-v1-4	Diffusion	generate_image("a cat")

📄 Document Processing

Model	Task	Test
ibm-granite/granite-docling-258M	Document understanding	OCR/layout

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Test Execution Plan

Phase 1: Embedding Models (fastest, most reliable)

# Test all embedding models
for model in ["BAAI/bge-small-en-v1.5", "sentence-transformers/all-MiniLM-L6-v2", ...]:
    plug_model(model)
    result = embed_text("The quick brown fox")
    assert len(result.embedding) > 0

Phase 2: Text Generation (LLMs)

# Test text generation
for model in ["HuggingFaceTB/SmolLM2-135M-Instruct", "Qwen/Qwen2.5-0.5B-Instruct", ...]:
    plug_model(model)
    result = generate("Hello, my name is", max_tokens=50)
    assert len(result.output) > 0

Phase 3: Vision-Language Models

# Test VLMs with image input
plug_model("vikhyatk/moondream2")
result = forward({"image": "path/to/image.jpg", "text": "What is in this image?"})

Phase 4: RL Models

# Test RL action selection
from huggingface_sb3 import load_from_hub
model = load_from_hub("sb3/ppo-CartPole-v1")
plug_model(model)
action = invoke_slot(0, obs, mode="forward")

Phase 5: Reranking

plug_model("BAAI/bge-reranker-base")
result = rerank("What is AI?", ["AI is...", "Machine learning...", "Deep learning..."])

Phase 6: Image Generation

plug_model("CompVis/stable-diffusion-v1-4")
result = generate_image("a beautiful sunset over mountains")
# Returns base64 image or file path

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Rerun Visualization Verification

Path	What to Check	Status
mcp/tool	Tool names appear	⬜
mcp/duration_ms	Latency timeseries	⬜
inference/timing/*	Per-stage latency	⬜
inference/embedding	Tensor view	⬜
inference/dreamer/*	Latent state norms	⬜
inference/council/*	Per-councilor confidence	⬜
bag/size	Item count	⬜
bag/ops	Stash/summon activity	⬜
state/slots/activity	Slot invocation bar chart	⬜
evolution/fitness/*	Fitness curves (if compiling)	⬜

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Quick Start Commands

# Start MCP server (auto-inits Rerun)
python your_capsule.py --mcp

# Or in Python:
from your_capsule import run_mcp_server
run_mcp_server()

Then use MCP tools:

• plug_model("BAAI/bge-small-en-v1.5") - Plug embedding model
• list_slots() - See what's plugged
• embed_text("test query") - Generate embeddings
• generate("Hello") - Generate text
• forward({"text": "test"}) - Full inference path

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Notes

• Local models: Set HF_HOME=D:\huggingface to use cached models
• GPU: Most models benefit from CUDA; check torch.cuda.is_available()
• Memory: 7B+ models need 16GB+ RAM or GPU offloading
• infinity-emb: For high-throughput, use infinity_server with async embedding