Ronin48LLC

selma-lora-adapter

README

License: apache-2.0

Model Details

Table with columns: Field, Value
Field	Value
Base Model	meta-llama/Llama-3.3-70B-Instruct
Adapter Type	LoRA (QLoRA)
LoRA Rank / Alpha / Dropout	64 / 128 / 0.05
Target Modules	`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
Task	Statute identification, element analysis, charge classification
Training Method	QLoRA supervised fine-tuning via `SFTTrainer` (trl)
License	Apache 2.0 (adapter weights)
Maintainer	Ronin48LLC

Capabilities

Given an incident description, SELMA can:

Statute Identification — which federal and/or state criminal statutes may have been violated, cited by title, chapter, and section
Element Analysis — the elements of each identified offense, mapped to specific facts in the incident
Charge Classification — felony/misdemeanor, degree, jurisdiction, mandatory minimums and maximums
Legal Reasoning — transparent chain-of-thought explaining why each statute applies or does not
Cross-Reference — related statutes, lesser included offenses, concurrent jurisdiction, federal/state overlap

Jurisdictions: U.S. Code Title 18 as the federal baseline, with state criminal codes layered on top. Priority states: Georgia (O.C.G.A. Title 16), California, Texas, New York, Florida.

Constitutional Override

The U.S. Constitution is the supreme law of the land, and SELMA is trained to know it. No statute, regulation, or agency policy overrides the Bill of Rights. Where SELMA identifies a potential charge that implicates constitutional protections — an unlawful search, a coerced confession, a due process violation — it says so plainly:

⚠ CONSTITUTIONAL CONCERN — evidence obtained through this method may be subject to suppression under the [Amendment]. SELMA recommends consulting with the prosecuting attorney before charging.

This is not a limitation. It is the feature.

Intended Use

For sworn law enforcement officers, detectives, and special agents:

Identifying potential charges from the facts of an incident
Understanding offense elements for report writing and probable-cause affidavits
Preliminary research on unfamiliar statutes
Training and scenario-based learning

SELMA outputs must be reviewed by a prosecutor or attorney before use in charging documents, arrest affidavits, or court filings.

Limitations

Read LIMITATIONS.md before deploying in any operational context. In short:

SELMA is not a licensed attorney; its outputs are not legal advice.
It has a training-data cutoff and cannot reflect later legislation or case law.
It does not model prosecutorial discretion, local diversion programs, or municipal ordinances.
It may reflect one federal circuit's view without flagging a split.
It works only from what you tell it, and it can hallucinate statute numbers and case citations — verify every citation against a primary source.

Usage

This is a PEFT LoRA adapter. Load it on top of the base model:

python

from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base_model = AutoModelForCausalLM.from_pretrained(    "meta-llama/Llama-3.3-70B-Instruct",    load_in_4bit=True,    device_map="auto",)model = PeftModel.from_pretrained(base_model, "Ronin48LLC/selma-lora-adapter")tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.3-70B-Instruct")

Note: Access to the base model requires accepting Meta's license on Hugging Face.

A prompt-only 8B stand-in (Llama 3.1 8B Instruct with SELMA's system prompt, no fine-tuning) is published as Ronin48LLC/selma.

Training

Framework: PyTorch + Transformers + TRL + PEFT, 4-bit quantized base
Adapter config: rank 64, alpha 128, dropout 0.05, all seven attention and MLP projections (see adapter_config.json)
Run details: training_args.bin is preserved in this repository; the step count and loss curve were not recorded in the upload

Training Data Sources

Table with columns: Source, Description, Size, License
Source	Description	Size	License
U.S. Code Title 18	Federal criminal statutes (USLM XML)	~2,700 sections	Public Domain
O.C.G.A. Title 16	Georgia criminal code	~500 sections	Fair Use
ALEA US Courts	Federal court filings with NOS codes	491K examples	Open
LegalBench	Legal reasoning benchmark tasks	91.8K examples	Open

Table with columns: Model, Domain, Use when…
Model	Domain	Use when…
SELMA (this adapter)	Law enforcement	Statute identification, charge elements, constitutional flags
ATTICUS	Public defense	SELMA's counterpart — every capability it gives law enforcement, in the hands of the public defender
ABBY	Forensic investigation	Digital and physical evidence, chain of custody, admissibility

Source

github.com/CryptoJones/SELMA — training pipeline, data collection, multi-state architecture, OWASP evaluation.

License

Available on FriendliAI

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Container

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Model Details

Model Provider

Ronin48LLC

Model Tree

Base

meta-llama/Llama-3.3-70B-Instruct

Adapter

this model

Input Modalities

Text

Output Modalities

Text

Supported Functionality

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README

License: apache-2.0

Model Details

Table with columns: Field, Value
Field	Value
Base Model	meta-llama/Llama-3.3-70B-Instruct
Adapter Type	LoRA (QLoRA)
LoRA Rank / Alpha / Dropout	64 / 128 / 0.05
Target Modules	`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
Task	Statute identification, element analysis, charge classification
Training Method	QLoRA supervised fine-tuning via `SFTTrainer` (trl)
License	Apache 2.0 (adapter weights)
Maintainer	Ronin48LLC

Capabilities

Given an incident description, SELMA can:

Statute Identification — which federal and/or state criminal statutes may have been violated, cited by title, chapter, and section
Element Analysis — the elements of each identified offense, mapped to specific facts in the incident
Charge Classification — felony/misdemeanor, degree, jurisdiction, mandatory minimums and maximums
Legal Reasoning — transparent chain-of-thought explaining why each statute applies or does not
Cross-Reference — related statutes, lesser included offenses, concurrent jurisdiction, federal/state overlap

Jurisdictions: U.S. Code Title 18 as the federal baseline, with state criminal codes layered on top. Priority states: Georgia (O.C.G.A. Title 16), California, Texas, New York, Florida.

Constitutional Override

⚠ CONSTITUTIONAL CONCERN — evidence obtained through this method may be subject to suppression under the [Amendment]. SELMA recommends consulting with the prosecuting attorney before charging.

This is not a limitation. It is the feature.

Intended Use

For sworn law enforcement officers, detectives, and special agents:

Identifying potential charges from the facts of an incident
Understanding offense elements for report writing and probable-cause affidavits
Preliminary research on unfamiliar statutes
Training and scenario-based learning

SELMA outputs must be reviewed by a prosecutor or attorney before use in charging documents, arrest affidavits, or court filings.

Limitations

Read LIMITATIONS.md before deploying in any operational context. In short:

SELMA is not a licensed attorney; its outputs are not legal advice.
It has a training-data cutoff and cannot reflect later legislation or case law.
It does not model prosecutorial discretion, local diversion programs, or municipal ordinances.
It may reflect one federal circuit's view without flagging a split.
It works only from what you tell it, and it can hallucinate statute numbers and case citations — verify every citation against a primary source.

Usage

This is a PEFT LoRA adapter. Load it on top of the base model:

python

from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base_model = AutoModelForCausalLM.from_pretrained(    "meta-llama/Llama-3.3-70B-Instruct",    load_in_4bit=True,    device_map="auto",)model = PeftModel.from_pretrained(base_model, "Ronin48LLC/selma-lora-adapter")tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.3-70B-Instruct")

Note: Access to the base model requires accepting Meta's license on Hugging Face.

A prompt-only 8B stand-in (Llama 3.1 8B Instruct with SELMA's system prompt, no fine-tuning) is published as Ronin48LLC/selma.

Training

Framework: PyTorch + Transformers + TRL + PEFT, 4-bit quantized base
Adapter config: rank 64, alpha 128, dropout 0.05, all seven attention and MLP projections (see adapter_config.json)
Run details: training_args.bin is preserved in this repository; the step count and loss curve were not recorded in the upload

Training Data Sources

Table with columns: Source, Description, Size, License
Source	Description	Size	License
U.S. Code Title 18	Federal criminal statutes (USLM XML)	~2,700 sections	Public Domain
O.C.G.A. Title 16	Georgia criminal code	~500 sections	Fair Use
ALEA US Courts	Federal court filings with NOS codes	491K examples	Open
LegalBench	Legal reasoning benchmark tasks	91.8K examples	Open

Table with columns: Model, Domain, Use when…
Model	Domain	Use when…
SELMA (this adapter)	Law enforcement	Statute identification, charge elements, constitutional flags
ATTICUS	Public defense	SELMA's counterpart — every capability it gives law enforcement, in the hands of the public defender
ABBY	Forensic investigation	Digital and physical evidence, chain of custody, admissibility

Source

github.com/CryptoJones/SELMA — training pipeline, data collection, multi-state architecture, OWASP evaluation.

selma-lora-adapter

README

Model Details

Capabilities

Constitutional Override

Intended Use

Limitations

Usage

Training

Training Data Sources

Related Models — the Ronin 48 suite

Source

License

Explore FriendliAI today

README

Model Details

Capabilities

Constitutional Override

Intended Use

Limitations

Usage

Training

Training Data Sources

Related Models — the Ronin 48 suite

Source

License