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Ronin48LLC
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Model Details
Model Provider
Ronin48LLC
Model Tree
meta-llama/Llama-3.3-70B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Field | Value |
|---|---|
| Base Model | meta-llama/Llama-3.3-70B-Instruct |
| Adapter Type | LoRA (QLoRA, 4-bit NF4) |
| LoRA Rank | 64 |
| Task | Forensic investigation, evidence analysis, investigative reasoning |
| Training Method | QLoRA fine-tuning via SFTTrainer (trl) |
| License | Apache 2.0 |
| Maintainer | Ronin48LLC |
ABBY is designed to assist certified forensic examiners, law enforcement investigators, and legal professionals with:
This model is intended for professional use in authorized law enforcement and forensic contexts only.
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/abby-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 HuggingFace.
ABBY is community-funded. Every contribution keeps this project free and in the hands of investigators who need it.
| Donor | Amount | Note |
|---|---|---|
| Ronin 48, LLC | N/A | Founding donor |
| Anonymous | $50 | Thank you, Joe Sixpack |
Want to support ABBY? Reach out to the maintainers.
Source: github.com/CryptoJones/ABBY
| Project | Description |
|---|---|
| SELMA | Law enforcement — criminal statute identification and charge elements |
| ATTICUS | Public defense — SELMA's counterpart: defense strategy and constitutional analysis |
| BONES | EMS — Biomedical On-scene Navigator for Emergency Services: protocols, dosing, triage |
| BRUNO | Fire service — Building Rescue and Unified Navigation Operations: tactics, hazmat, ICS |
Fine-tuned adapter weights are licensed under Apache 2.0. Base model weights are subject to the Meta Llama 3.3 Community License.
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/abby-lora-adapter")tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.3-70B-Instruct")