Instructions to use CaleraLabs/patentsec-12b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use CaleraLabs/patentsec-12b-gguf with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf CaleraLabs/patentsec-12b-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf CaleraLabs/patentsec-12b-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf CaleraLabs/patentsec-12b-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf CaleraLabs/patentsec-12b-gguf:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf CaleraLabs/patentsec-12b-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf CaleraLabs/patentsec-12b-gguf:Q4_K_M
Use Docker
docker model run hf.co/CaleraLabs/patentsec-12b-gguf:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use CaleraLabs/patentsec-12b-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CaleraLabs/patentsec-12b-gguf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CaleraLabs/patentsec-12b-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/CaleraLabs/patentsec-12b-gguf:Q4_K_M
- Ollama
How to use CaleraLabs/patentsec-12b-gguf with Ollama:
ollama run hf.co/CaleraLabs/patentsec-12b-gguf:Q4_K_M
- Unsloth Desktop
- Pi
How to use CaleraLabs/patentsec-12b-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf CaleraLabs/patentsec-12b-gguf:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "CaleraLabs/patentsec-12b-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use CaleraLabs/patentsec-12b-gguf with Docker Model Runner:
docker model run hf.co/CaleraLabs/patentsec-12b-gguf:Q4_K_M
- Lemonade
How to use CaleraLabs/patentsec-12b-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull CaleraLabs/patentsec-12b-gguf:Q4_K_M
Run and chat with the model
lemonade run user.patentsec-12b-gguf-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use CaleraLabs/patentsec-12b-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf CaleraLabs/patentsec-12b-gguf:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default CaleraLabs/patentsec-12b-gguf:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use CaleraLabs/patentsec-12b-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf CaleraLabs/patentsec-12b-gguf:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "CaleraLabs/patentsec-12b-gguf:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
PatentSec 12B (GGUF)
High-Precision Patent Prosecution & Statutory Reasoning Model (Gemma 4 12B)
Calera Computing, Inc. β’ Zenodo Preprint DOI: 10.5281/zenodo.23520273
1. Overview
PatentSec-12B (gemma-4-12b-patent-bedrock) is a domain-specialized patent prosecution fine-tune of Google Gemma 4 12B Instruction (google/gemma-4-12B-it). This repository provides quantized GGUF weights (Q4_K_M and Q8_0) for local inference with llama.cpp, Ollama, or LM Studio, alongside patentsec.py, a lightweight Python client for Calera's deterministic statutory solvers.
- Standalone GGUF / Weights Performance (
77.0%Overall /96.5%on Self-Contained Tasks): Running standalone from weights alone on the frozen 283-casePatentBench v0.2.0Layer 1 Mini split, PatentSec-12B achieves 77.0% (218/283) overallβand 96.5% (218/226) across all self-contained statutory tasks where the scenario or document is in the prompt, including 100.0% (90/90) on USPTO Extension Fee Schedules, 100.0% (37/37) on Office Action Classification, 92.0% (23/25) on Filing Date Extraction, and 91.9% (68/74) on Statutory Deadlines. - Compound System with ICX Substrate (
92.6%on Mini /99.23%on Full 7,097-Case Enterprise Corpus): The remaining benchmark items query external file-wrapper facts (such as the assigned Examiner or Technology Center for a bare application number) without providing the docket in the prompt. Pairing PatentSec-12B with the hosted ICX Epistemic Memory & Statutory Solver Loop (patent.caleralabs.com) achieves 92.6% (262/283) on the 283-case Mini split and 99.23% (7,042/7,097 verified passes) across the exhaustive 7,097-casePatentBench v0.2.0Full Enterprise Corpus.
2. USPTO Data Scope & Temporal Coverage
PatentSec-12B is engineered as a statutory reasoning and claim-auditing engine focused on modern USPTO prosecution (2015β2026), rather than an archival database of all historical USPTO patent years (1790βpresent). The table below details the exact scope of the model weights, the hosted ICX record store, and the included Python client:
| Component | USPTO Coverage & Scale | Temporal Window | Core Capabilities | Scope Boundary |
|---|---|---|---|---|
1. PatentSec-12B Weights (this repo: .gguf) |
Domain-specialized patent prosecution & statutory dialect fine-tune (G03F, G06F, G06Q, G21B, H01L utility exemplars) |
2015β2024 utility filings & grants 2026 USPTO eCFR fee schedule |
β’ Pennwalt All-Elements claim limitation auditing (35 U.S.C. Β§ 271(a)) β’ 35 U.S.C. Β§ 112(b) / MPEP Β§ 2173.05(e) antecedent basis verification β’ 37 CFR Β§Β§ 1.16β1.18 / 1.136 extension fee schedules (1β5 months; Large/Small/Micro) β’ 35 U.S.C. Β§ 21(b) & Β§ 154(a)(2) deadline and 20-year term calculation β’ Zero-confabulation refusal ($\partial^2 = 0$) of nonexistent/out-of-horizon patent numbers (>12.2M) |
Designed for reasoning over prompt-supplied claims and office actions, not for memorizing historical USPTO file wrappers across all years. |
2. Hosted ICX Record Store (patent.caleralabs.com) |
395 PEDS-hydrated applications (16/100,000β18/500,079 series covering the PatentBench corpus) + 52 canonical test grants + 6 landmark patents (US 5,960,411 to US 11,545,671) + 2020β2026 grant shards |
2018-08-09 to 2023-11-01 filings 2019-02-19 to 2026-04-21 grants 2018-11 to 2026-03 prosecution events |
Instant recall of application_number, title, filing_date, grant_date, art_unit, technology_center, examiners, and Office Action mailing dates/codes for the indexed 2018β2026 cohort. |
Scoped to the modern 2018β2026 prosecution window (plus landmark grants). For older pre-2018 series (08/β15/) or unindexed dockets, supply the text in the prompt. |
3. Python Solver Client (patentsec.py) |
Deterministic statutory rule & solver client | 2026-10-01 eCFR fee table; arbitrary ISO dates for calendar math | Exact 35 U.S.C. Β§ 21(b) / 37 CFR Β§ 1.7 federal holiday/weekend deadline rollover, 37 CFR Β§ 1.17(a) fee lookups, and All-Elements claim chart verification. | Evaluates caller-supplied claims and dates; does not bundle an offline USPTO patent archive. |
Best Practice for Local Inference: When running
PatentSec-12Blocally inllama.cpporOllama, achieve 96%+ statutory accuracy on any patent from any era by pasting the relevant claim text, Office Action excerpt, or filing dates directly into your prompt (or pairing the model with your own USPTO PEDS / PatentsView RAG pipeline).
3. Repository Files
| File | Quantization | Size | SHA-256 |
|---|---|---|---|
gemma-4-12b-patent-bedrock.Q4_K_M.gguf |
Q4_K_M | 7.38 GB | 35a878bfc42f540adbc8f72d04b8616b5c1ad6016cd1b555cb6bfc3b10eaef13 |
gemma-4-12b-patent-bedrock.Q8_0.gguf |
Q8_0 | 12.67 GB | 1b646919ab9705e453159ddc098c0244ca9d67ab7adeff0db02bf604cf6347df |
Modelfile |
β | 769 B | Ollama Modelfile for ollama create patentsec-12b -f Modelfile |
patentsec.py |
β | 20.5 KB | Python client for deterministic statutory solvers (Section 5) |
requirements.txt |
β | 17 B | Minimal dependency specification (requests>=2.28.0) |
4. Benchmark Evaluation
We report two complementary PatentBench v0.2.0 evaluations:
- The 283-Case Layer 1 Mini Split: A fast, frozen benchmark slice for local practitioners to evaluate the standalone 12B weights (
77.0%overall /96.5%on self-contained tasks) directly against the hosted ICX compound system (92.6%). - The 7,097-Case Full Enterprise Corpus: The exhaustive Layer 1 evaluation of the full live
PatentSec-12B + ICX Substrate(99.23%overall accuracy:7,042 / 7,097verified passes).
4.1 PatentBench v0.2.0 β Layer 1 Mini Split (283 Cases: Standalone Weights vs. ICX)
- Dataset: PatentBench v0.2.0, 283-case Layer 1 Mini split.
- Evaluator: Unmodified
patentbench==0.2.0DeterministicEvaluatoracross all 283 Layer 1 deterministic cases (temperature=0,bf16on vLLM).
| Task Pillar | PatentSec-12B Alone (these weights) |
ICX + PatentSec-12B (hosted system) |
Notes |
|---|---|---|---|
Fee Schedule (37 CFR Β§ 1.17) |
100.0% (90/90) | 100.0% (90/90) | Zero errors across all extension tiers & entity statuses from weights alone |
Statutory Deadlines (35 U.S.C. Β§ 21(b)) |
91.9% (68/74) | 98.6% (73/74) | ICX adds deterministic calendar-month clamp & federal holiday rollover |
Classification & Tech Center (OA + TC) |
67.3% (37/55) | 98.2% (54/55) | 12B Alone is 37/37 (100%) on Office Action text; ICX adds Art Unit β TC lookup (13/13) |
Examiner Extraction (File Wrapper) |
0.0% (0/19) | 94.7% (18/19) | Requires external application record lookup (2018β2026 PEDS store) |
Timeline & Filing Dates (Prosecution) |
51.1% (23/45) | 60.0% (27/45) | 12B Alone scores 23/25 (92.0%) when filing date context is in the prompt |
| Self-Contained Prompt Tasks (226 Cases) | 96.5% (218/226) | 99.1% (224/226) | All cases where the prompt supplies the statutory scenario or text |
| Overall Layer 1 Mini (All 283 Cases) | 77.0% (218/283) | 92.6% (262/283) | +15.6% overall lift when paired with live USPTO record recall |
4.2 PatentBench v0.2.0 β Full Enterprise Corpus (7,097 Cases: Live PatentSec-12B + ICX Substrate)
- Dataset: Exhaustive 7,097-case Layer 1 Full Enterprise Corpus (spanning 395 real-world USPTO utility applications from the 2018β2026 cohort).
- System Evaluated: Full production PatentSec-12B + ICX Epistemic Substrate (
patent.caleralabs.com, combining the 12B neural viewport with the 4D simplicial $e.\text{mem}$ lattice and deterministic Go statutory solvers). - Reference: The Epistemic Substrate: Achieving 99.2% Deterministic Accuracy on 7,000+ Real-World Legal Cases with a 12B Parameter Neural Viewport (Zenodo DOI
10.5281/zenodo.23520273).
| Operational Category | Verified Passes | Accuracy |
|---|---|---|
Statutory Fee Schedules (37 CFR Β§ 1.17) |
2,050 / 2,050 | 100.0% |
| Regulatory Office Action Classification | 1,693 / 1,693 | 100.0% |
| Β§ 103 Obviousness Strategy Formulation | 269 / 269 | 100.0% |
Statutory Response Deadlines (35 U.S.C. Β§ 21) |
2,030 / 2,031 | 99.95% |
| Prosecution History Timeline Parsing | 1,000 / 1,055 | 94.79% |
| Total Full Enterprise Corpus (Layer 1) | 7,042 / 7,097 | 99.23% |
4.3 Statutory Compliance & Boundary Refusal Capabilities
Key statutory verification capabilities when supplied with claim text or paired with the ICX substrate:
- 35 U.S.C. Β§ 112(b) Antecedent Basis Defect Catch: 100.0% (autonomous dependency chain tracing back to parent independent claims under MPEP Β§ 2173.05(e)).
- Pennwalt All-Elements Rule Precision: 100.0% (verbatim limitation isolation and open-ended
comprisingpreamble parsing when claim text is provided). - Adversarial Out-of-Horizon Refusal ($\partial^2 = 0$): 100.0% refusal / 0.00% confabulation on nonexistent patent traps (e.g.,
US 99,999,999 B2,US 22,001,110 A1).
5. Usage
5.1 Local Inference with llama.cpp or Ollama
# llama.cpp interactive chat (uses the chat template embedded in the GGUF)
llama-cli -m gemma-4-12b-patent-bedrock.Q4_K_M.gguf --jinja -cnv -c 4096 -t 8 --temp 0
# Ollama local deployment using the included Modelfile
ollama create patentsec-12b -f Modelfile
ollama run patentsec-12b
5.2 Deterministic Statutory Tools (patentsec.py)
patentsec.py connects to Calera's hosted public IP-rate-limited gateway (set CALERA_API_KEY from dashboard.caleralabs.com for dedicated enterprise rate limits) and includes local offline fallback for statutory deadlines, fee schedules, and claim limitation matching.
git clone https://huggingface.co/CaleraLabs/patentsec-12b-gguf
cd patentsec-12b-gguf
pip install -r requirements.txt
python patentsec.py --demo
*All-Elements Rule check (Pennwalt Corp. v. Durand-Wayland, Inc.):*
from patentsec import PatentSecClient
client = PatentSecClient()
claim = [
"an image sensor",
"an anamorphic telecentric lens focusing light onto the image sensor",
"an FPGA-based hardware sorter coupled to the image sensor",
"a pneumatic ejector valve triggered by the FPGA-based hardware sorter",
]
accused = [
"an image sensor (CMOS, 2048 px line scan)",
"a conventional spherical lens focusing light onto the image sensor",
"an FPGA-based hardware sorter coupled to the image sensor",
"a pneumatic ejector valve triggered by the FPGA-based hardware sorter within 40 microseconds",
]
result = client.verify_all_elements_rule(claim, accused)
print(result["missing_limitations"])
# -> ['an anamorphic telecentric lens focusing light onto the image sensor']
print(result["literal_infringement"])
# -> False
Reply deadlines (35 U.S.C. Β§ 21(b), 37 CFR Β§ 1.7):
deadline = client.calculate_statutory_deadline("2025-11-15", period_months=3)
print(deadline["statutory_response_deadline"])
# -> "2026-02-17" (2026-02-15 is a Sunday; 2026-02-16 is Washington's Birthday)
Extension fees (37 CFR Β§ 1.17(a), 2026-10-01 eCFR schedule):
print(client.evaluate_fee_schedule("SMALL", extension_months=2)["statutory_fee_usd"])
# -> 276
6. Intended Use & Professional Responsibility
- Intended Use: Patent prosecution research, claim limitation charting, 35 U.S.C. Β§ 112(b) antecedent basis auditing, and statutory deadline/fee verification, either standalone (with prompt-supplied claims/docket text) or paired with USPTO record retrieval.
- Not Legal Advice: Outputs do not constitute legal advice, formal claim construction, or a Freedom-to-Operate (FTO) legal opinion, and must be reviewed by a registered patent practitioner.
- USPTO Record Verification: Because the standalone weights do not store historical USPTO file wrappers across all patent years (Section 2), always supply the relevant patent text in your prompt or pair the model with a verified USPTO retrieval store, and check official records in USPTO Patent Center.
7. Citation
@techreport{race2026patentsec,
title={The Epistemic Substrate: Achieving 99.2% Deterministic Accuracy on 7,000+ Real-World Legal Cases with a 12B Parameter Neural Viewport},
author={Race, Casey Lee and Calera Research Team},
institution={Calera Computing, Inc.},
doi={10.5281/zenodo.23520273},
year={2026},
url={https://doi.org/10.5281/zenodo.23520273}
}
Developed by Calera Computing, Inc.
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Evaluation results
- Layer 1 accuracy, 283 cases (bf16, vLLM, model alone) on PatentBench v0.2.0 (Layer 1 Mini Split)self-reported77.000