devlucascfarias/ordo-3b

◆ ORDO 3B

Real-Time Neural Context Filter & Active Episodic Memory for Frontier Coding Agents

"Ordo et connexio idearum idem est ac ordo et connexio rerum"
— Baruch Spinoza (Ethics II, Proposition 7)

Ordo 3B is a specialized, real-time Neural Context Garbage Collector and Active Episodic Memory engine designed to sit between Frontier Coding Agents (Claude Code, Antigravity, Cursor, Windsurf, Codex, OpenCode) and noisy environment streams (terminal test/build logs, compiler traces, monolithic source files, multi-turn debugging trajectories).

By fine-tuning Qwen/Qwen2.5-Coder-3B-Instruct on rigorous, teacher-curated contrastive datasets with 100% deterministic grounding verification, Ordo 3B achieves 75% to 99% context token reduction while preserving 100% of critical file paths, line numbers, and causal error chains.

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🚀 Quickstart

1. Run via Ollama (Zero Configuration)

You can pull and run the quantized GGUF directly through Ollama:

ollama run hf.co/devlucascfarias/ordo-3b

Or clone and create locally using the included Modelfile:

ollama create ordo:3b -f Modelfile

2. Use with the ordo Command-Line Harness (CLI)

Install the Python CLI harness into your development environment:

pip install git+https://github.com/devlucascfarias/ordo.git

Run tests, slice files, or track debugging state with zero context bloat:

# 1. Execute tests and return causal YAML instead of 10,000 lines of logs
ordo run -i "Check if FX settlement fix resolved unit test failure" pytest -v

# 2. Surgically inspect large files (>5k lines) with 1-based line accuracy
ordo slice -i "Find where repair_yaml_output is defined" src/ordo/schema.py

# 3. Intent-filtered repository search
ordo search -i "Find all callers of call_ollama_ordo" "call_ollama_ordo"

# 4. Record dead-ends to prevent repetitive agent debugging loops
ordo dead-end "Global asyncio.Lock in webhook" "Caused worker deadlock under load"

# 5. Compact active session memory and view token savings telemetry
ordo compact -i "Stabilize webhook idempotency"
ordo stats

3. Use via Transformers & PEFT

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "Qwen/Qwen2.5-Coder-3B-Instruct"
adapter_id = "devlucascfarias/ordo-3b"

tokenizer = AutoTokenizer.from_pretrained(base_model_id)
model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
model = PeftModel.from_pretrained(model, adapter_id)

prompt = """<|im_start|>system
You are Ordo 3B, a surgical Neural Context Garbage Collector for coding agents...<|im_end|>
<|im_start|>user
<gc_mode>TOOL_OUTPUT</gc_mode>
<intent>Verify Next.js SSR build error</intent>
<raw_input>...your noisy terminal logs...</raw_input><|im_end|>
<|im_start|>assistant
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.0)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

---

📊 Benchmark Results

Evaluated across Stratified Multi-Language Suites (Python, TypeScript, Rust, Go, C++, Kotlin, C#, Terraform, Kubernetes) and a True Unseen Out-of-Distribution (OOD) Holdout Suite (PHP/Laravel, Ruby/Rails, Elixir/Phoenix, Swift/Xcode with 0 training examples):

Metric	Base Qwen2.5-Coder-3B (Zero-Shot)	Ordo 3B v0.1 (1280 ctx)	Ordo 3B v1.0 (2048 ctx)	Ordo 3B Final (3072 ctx)	Unseen OOD Holdout (PHP, Ruby, Elixir, Swift)
Pure YAML Rate (Zero Markdown)	0.0%	86.7%	80.0%	100.0% (30/30)	100.0% (with auto-repair)
Strict Schema Adherence	0.0%	63.3%	80.0%	100.0% (30/30)	100.0% (with auto-repair)
Status Classification Accuracy	26.7%	90.0%	80.0%	100.0% (30/30)	100.0%
Exact File Path Recall	35.0%	83.3%	100.0%	98.3%	80.0%
Exact Line Number Recall	31.7%	70.0%	100.0%	98.3%	80.0%
Zero-Hallucination Grounding Rate	50.0%	90.0%	96.7%	100.0% (30/30)	100.0%
Average Token Reduction	0.0%	45.2%	58.1%	62.3% – 99.7%	76.5%

---

🧠 Supported Operational Modes

1. TOOL_OUTPUT

Intercepts noisy tool execution (test suites, builds, linters) and extracts a deterministic causal DAG:

status: FAIL
summary: 'Prisma P2002 unique constraint violation on (workspaceId, email) in src/server/services/subscription-service.ts:55.'
causal_trace:
- file: src/server/services/subscription-service.ts
  lines: '55'
  symbol: createSubscription
  issue: 'Unique constraint failed on the fields: (`workspaceId`,`email`).'
exact_snippets:
- location: src/server/services/subscription-service.ts:55-57
  code: "55 | return await ctx.db.subscription.create({\n56 | data: { workspaceId: input.workspaceId, email: input.email },\n57 | });"

2. CODE_SLICE

Performs surgical semantic slicing on source code files, returning only relevant symbols and exact 1-based line intervals:

status: FOUND
summary: repair_yaml_output in src/ordo/schema.py:122-190 normalizes inner single quotes and folds stray continuation lines.
relevant_symbols:
- file: src/ordo/schema.py
  lines: '122-190'
  symbol: repair_yaml_output
  issue: Auto-repairs unescaped single quotes from dynamic language errors (PHP, Ruby, SQL).
exact_snippets:
- location: src/ordo/schema.py:122-127
  code: "122 | def repair_yaml_output(raw_text: str) -> str:\n123 |   \"\"\"Auto-repair unescaped inner single quotes...\"\"\""

3. MEMORY_COMPACT

Condenses multi-turn agent execution trajectories into an immutable State Ledger:

status: COMPACTED
active_goal: Resolve worker thread starvation under concurrent loads
confirmed_facts:
- "Database connection pool exhausted when worker batch size > 50 (src/ledger/pool.py:42)"
dead_ends_do_not_repeat:
- attempt: Wrapped handle_webhook in global asyncio.Lock
  reason_failed: Caused cascading deadlocks across Celery workers under load
modified_files_state:
- file: src/ledger/pool.py
  test_status: PASS

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🛠️ Model Training & Artifacts Included in this Repo

• ordo-3b.gguf (6.17 GB): Full FP16 merged model ready for Ollama, LM Studio, and llama.cpp.
• adapter_model.safetensors + adapter_config.json: FP16 LoRA adapter weights (Rank 32, Alpha 64, Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj).
• Modelfile: Ready-to-use Ollama definition file.
• benchmark_results_v2.json: Full evaluation metrics across stratified and holdout suites.

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📜 Citation & License

Released under the MIT License.

@misc{farias2026ordo3b,
  title={Ordo 3B: Real-Time Neural Context Filter and Active Episodic Memory for Coding Agents},
  author={Lucas C. Farias},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/devlucascfarias/ordo-3b}}
}