Ronin48LLC

bones-lora-adapter

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	EMS protocol lookup, drug reference, triage and differential support
Training Method	QLoRA supervised fine-tuning via `SFTTrainer` (trl)
License	MIT (adapter weights)
Maintainer	Ronin48LLC

Capabilities

Protocol lookup — AHA ACLS/PALS/BLS algorithms, NWC protocols, NREMT scope of practice
Drug reference — EMS formulary, weight-based dosing, contraindications, interactions
Triage support — START/SALT/JumpSTART, mass-casualty incident guidance
Differential support — symptom-to-differential reasoning for field assessment
Trauma guidance — hemorrhage control, spinal precautions, burn classification
OB/Peds — childbirth emergencies, pediatric dosing (Broselow), neonatal resuscitation
Toxicology — overdose recognition, antidote references, decontamination
Documentation — PCR narrative generation, patient-assessment templates

Intended Use

For certified EMS personnel operating under medical direction:

Protocol lookup and reference during training and non-emergency preparation
Differential considerations for patient presentations
Drug reference and dosing ranges — verified against your formulary before use
Triage category guidance (START/SALT)
PCR documentation assistance
Training and scenario-based learning

BONES is a clinical decision support tool, not a replacement for medical direction. When in doubt, call medical control.

Limitations

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

BONES is not a physician; its outputs are not medical advice. All clinical decisions are made by a licensed provider under medical direction.
It does not replace regional protocols, offline medical direction, or base-hospital contact requirements.
It cannot assess the patient — it works from what you tell it.
It answers across the full EMR–Paramedic spectrum; you are responsible for operating within your certified scope.
It is not an FDA-cleared medical device and has not undergone clinical validation.
It has a training-data cutoff; AHA guideline updates and protocol changes after it may not be reflected.
Drug dosing must always be verified — pediatric weight-based dosing, maximum doses, and contraindications — against your agency formulary before administration.

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/bones-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.

The base model with the BONES system prompt and no fine-tuning is a workable baseline; see the source repository's Quick Start.

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, License
Source	Description	License
AHA ACLS/PALS/BLS Guidelines	Cardiac arrest and resuscitation algorithms	Public guidelines
NAEMSP / NASEMSO Protocols	National EMS protocols and scope of practice	Public
OpenMedSpel / MedQA	Medical Q&A datasets	Open
PubMed Central	EMS and emergency-medicine literature	Open Access
NREMT Scope of Practice	EMR/EMT/AEMT/Paramedic scope tables	Public
Synthetic Scenarios	AI-generated dispatch-to-treatment examples	Proprietary

BONES, BRUNO, and SELMA share scenes constantly — consult the appropriate model for each domain.

Table with columns: Model, Domain, Use when…
Model	Domain	Use when…
BONES (this adapter)	EMS — EMR / EMT / AEMT / Paramedic	Patient assessment, treatment protocols, drug dosing, triage, transport
BRUNO	Fire service — company officer / IC	Fireground tactics, size-up, hazmat, extrication, water supply, ICS
SELMA	Law enforcement	Criminal statute identification, charge elements, constitutional flags

Table with columns: Shared scene, Primary, Support
Shared scene	Primary	Support
Structure fire with casualties	BRUNO (fireground ops)	BONES (patient care)
Vehicle accident with entrapment	BRUNO (extrication)	BONES (care during extrication)
Hazmat with patient exposures	BRUNO (mitigation, decon zone)	BONES (patient decon and treatment)
Mass casualty incident	BONES (triage, treatment)	BRUNO (ICS, sectors) + SELMA (criminal nexus)
Overdose call	BONES (patient care, naloxone)	SELMA (distribution charges if applicable)
Cardiac arrest in a burning structure	BRUNO (scene safety, egress)	BONES (resuscitation protocol)

ABBY (forensics) operates independently of the first-responder suite; SELMA pairs with ATTICUS on the legal side.

Source

github.com/CryptoJones/BONES — data collection, QLoRA pipeline, synthetic-scenario generator.

License

Available on FriendliAI

Dedicated Endpoints

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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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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	EMS protocol lookup, drug reference, triage and differential support
Training Method	QLoRA supervised fine-tuning via `SFTTrainer` (trl)
License	MIT (adapter weights)
Maintainer	Ronin48LLC

Capabilities

Protocol lookup — AHA ACLS/PALS/BLS algorithms, NWC protocols, NREMT scope of practice
Drug reference — EMS formulary, weight-based dosing, contraindications, interactions
Triage support — START/SALT/JumpSTART, mass-casualty incident guidance
Differential support — symptom-to-differential reasoning for field assessment
Trauma guidance — hemorrhage control, spinal precautions, burn classification
OB/Peds — childbirth emergencies, pediatric dosing (Broselow), neonatal resuscitation
Toxicology — overdose recognition, antidote references, decontamination
Documentation — PCR narrative generation, patient-assessment templates

Intended Use

For certified EMS personnel operating under medical direction:

Protocol lookup and reference during training and non-emergency preparation
Differential considerations for patient presentations
Drug reference and dosing ranges — verified against your formulary before use
Triage category guidance (START/SALT)
PCR documentation assistance
Training and scenario-based learning

BONES is a clinical decision support tool, not a replacement for medical direction. When in doubt, call medical control.

Limitations

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

BONES is not a physician; its outputs are not medical advice. All clinical decisions are made by a licensed provider under medical direction.
It does not replace regional protocols, offline medical direction, or base-hospital contact requirements.
It cannot assess the patient — it works from what you tell it.
It answers across the full EMR–Paramedic spectrum; you are responsible for operating within your certified scope.
It is not an FDA-cleared medical device and has not undergone clinical validation.
It has a training-data cutoff; AHA guideline updates and protocol changes after it may not be reflected.
Drug dosing must always be verified — pediatric weight-based dosing, maximum doses, and contraindications — against your agency formulary before administration.

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/bones-lora-adapter")tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.3-70B-Instruct")

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/bones-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.

The base model with the BONES system prompt and no fine-tuning is a workable baseline; see the source repository's Quick Start.

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, License
Source	Description	License
AHA ACLS/PALS/BLS Guidelines	Cardiac arrest and resuscitation algorithms	Public guidelines
NAEMSP / NASEMSO Protocols	National EMS protocols and scope of practice	Public
OpenMedSpel / MedQA	Medical Q&A datasets	Open
PubMed Central	EMS and emergency-medicine literature	Open Access
NREMT Scope of Practice	EMR/EMT/AEMT/Paramedic scope tables	Public
Synthetic Scenarios	AI-generated dispatch-to-treatment examples	Proprietary

BONES, BRUNO, and SELMA share scenes constantly — consult the appropriate model for each domain.

Table with columns: Model, Domain, Use when…
Model	Domain	Use when…
BONES (this adapter)	EMS — EMR / EMT / AEMT / Paramedic	Patient assessment, treatment protocols, drug dosing, triage, transport
BRUNO	Fire service — company officer / IC	Fireground tactics, size-up, hazmat, extrication, water supply, ICS
SELMA	Law enforcement	Criminal statute identification, charge elements, constitutional flags

Table with columns: Shared scene, Primary, Support
Shared scene	Primary	Support
Structure fire with casualties	BRUNO (fireground ops)	BONES (patient care)
Vehicle accident with entrapment	BRUNO (extrication)	BONES (care during extrication)
Hazmat with patient exposures	BRUNO (mitigation, decon zone)	BONES (patient decon and treatment)
Mass casualty incident	BONES (triage, treatment)	BRUNO (ICS, sectors) + SELMA (criminal nexus)
Overdose call	BONES (patient care, naloxone)	SELMA (distribution charges if applicable)
Cardiac arrest in a burning structure	BRUNO (scene safety, egress)	BONES (resuscitation protocol)

ABBY (forensics) operates independently of the first-responder suite; SELMA pairs with ATTICUS on the legal side.

Source

github.com/CryptoJones/BONES — data collection, QLoRA pipeline, synthetic-scenario generator.

bones-lora-adapter

README

Model Details

Capabilities

Intended Use

Limitations

Usage

Training

Training Data Sources

Source

License

Explore FriendliAI today

README

Model Details

Capabilities

Intended Use

Limitations

Usage

Training

Training Data Sources

Source

License

bones-lora-adapter

README

Model Details

Capabilities

Intended Use

Limitations

Usage

Training

Training Data Sources

Related Models — the Ronin 48 first-responder suite

Source

License

Explore FriendliAI today

README

Model Details

Capabilities

Intended Use

Limitations

Usage

Training

Training Data Sources

Related Models — the Ronin 48 first-responder suite

Source

License