Instructions to use Ronin48LLC/abby-lora-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ronin48LLC/abby-lora-adapter with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.3-70B-Instruct")
model = PeftModel.from_pretrained(base_model, "Ronin48LLC/abby-lora-adapter")

Transformers

How to use Ronin48LLC/abby-lora-adapter with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Ronin48LLC/abby-lora-adapter")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Ronin48LLC/abby-lora-adapter", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ronin48LLC/abby-lora-adapter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ronin48LLC/abby-lora-adapter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ronin48LLC/abby-lora-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ronin48LLC/abby-lora-adapter

SGLang

How to use Ronin48LLC/abby-lora-adapter with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Ronin48LLC/abby-lora-adapter" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ronin48LLC/abby-lora-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Ronin48LLC/abby-lora-adapter" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ronin48LLC/abby-lora-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ronin48LLC/abby-lora-adapter with Docker Model Runner:
```
docker model run hf.co/Ronin48LLC/abby-lora-adapter
```

Ronin48LLC commited on 25 days ago

Commit

925b6e7

verified ·

1 Parent(s): 2fd2342

Add model card with full documentation

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@@ -1,209 +1,117 @@
 ---
 base_model: meta-llama/Llama-3.3-70B-Instruct
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:meta-llama/Llama-3.3-70B-Instruct
 - lora
 - sft
 - transformers
 - trl
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]
-### Framework versions
-- PEFT 0.19.1

 ---
 base_model: meta-llama/Llama-3.3-70B-Instruct
+language:
+- en
+license: apache-2.0
 library_name: peft
 pipeline_tag: text-generation
 tags:
 - lora
+- qlora
 - sft
+- peft
 - transformers
 - trl
+- forensics
+- law-enforcement
+- investigative-ai
 ---
+# ABBY — Artifact, Ballistic, and Binary Yield
+> *"I use my powers for good, not evil."*
+> — Abby Sciuto, *NCIS*
+**An open-source LoRA adapter fine-tuned for law enforcement forensic investigators.**
+ABBY is a QLoRA adapter trained on top of [Meta Llama 3.3 70B Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct), specialized for forensic analysis, evidence interpretation, ballistic assessment, digital artifact examination, and investigative reasoning.
+---
+## Model Details
+| Field | Value |
+|---|---|
+| **Base Model** | [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/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](https://huggingface.co/Ronin48LLC) |
+---
+## Intended Use
+ABBY is designed to assist **certified forensic examiners, law enforcement investigators, and legal professionals** with:
+- **Digital forensics** — artifact recovery, file system analysis, metadata examination
+- **Ballistic analysis** — wound pattern assessment, trajectory reconstruction
+- **Binary/malware analysis** — code review, threat identification
+- **Chain of custody guidance** — evidence handling best practices
+- **Investigative reasoning** — connecting evidence to conclusions
+**This model is intended for professional use in authorized law enforcement and forensic contexts only.**
+---
+## Usage
+This is a PEFT LoRA adapter. Load it on top of the base model:
+```python
+from peft import PeftModel
+from 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.
+---
+## Training
+- **Framework:** PyTorch 2.4 + Transformers + TRL + PEFT
+- **Quantization:** 4-bit NF4 (bitsandbytes)
+- **Hardware:** NVIDIA A100 PCIe 80GB
+- **Training steps:** 78
+- **Sequence length:** 4096
+---
+## Supporters
+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.*
+---
+## Related Projects
+| Project | Description |
+|---|---|
+| [SELMA](https://codeberg.org/Ronin48/SELMA) | Criminal law and statute analysis |
+| [ATTICUS](https://codeberg.org/Ronin48/ATTICUS) | Legal defense reasoning |
+| [BONES](https://codeberg.org/Ronin48/BONES) | Forensic pathology and osteology |
+| [BRUNO](https://codeberg.org/Ronin48/BRUNO) | Field operations and tactical support |
+---
+## License
+Fine-tuned adapter weights are licensed under **Apache 2.0**.
+Base model weights are subject to the [Meta Llama 3.1 Community License](https://llama.meta.com/llama3/license/).