Instructions to use briankkogi/gemma3-phishing-v1-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use briankkogi/gemma3-phishing-v1-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="briankkogi/gemma3-phishing-v1-bf16")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("briankkogi/gemma3-phishing-v1-bf16")
model = AutoModelForMultimodalLM.from_pretrained("briankkogi/gemma3-phishing-v1-bf16", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use briankkogi/gemma3-phishing-v1-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "briankkogi/gemma3-phishing-v1-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "briankkogi/gemma3-phishing-v1-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/briankkogi/gemma3-phishing-v1-bf16

SGLang

How to use briankkogi/gemma3-phishing-v1-bf16 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 "briankkogi/gemma3-phishing-v1-bf16" \
    --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": "briankkogi/gemma3-phishing-v1-bf16",
		"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 "briankkogi/gemma3-phishing-v1-bf16" \
        --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": "briankkogi/gemma3-phishing-v1-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use briankkogi/gemma3-phishing-v1-bf16 with Docker Model Runner:
```
docker model run hf.co/briankkogi/gemma3-phishing-v1-bf16
```

Gemma 3 Phishing Classifier (Merged BF16)

This repository contains the merged full model (base Gemma + LoRA adapter merged into dense weights) for binary phishing classification (phishing vs safe).

Merged from:
- base: google/gemma-3-4b-it
- adapter: briankkogi/gemma3-phishing-main-v1
Precision target at merge time: BF16

Model Type

Type: full merged Causal LM checkpoint (no PEFT adapter loading needed)
Output labels: phishing or safe (single-word target)

What This Model Is For

This model is intended for direct deployment where a single merged checkpoint is preferred over base+adapter composition.

Quickstart (Transformers)

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

repo = "briankkogi/gemma3-phishing-v1-bf16"

tok = AutoTokenizer.from_pretrained(repo)
if tok.pad_token is None:
    tok.pad_token = tok.eos_token

model = AutoModelForCausalLM.from_pretrained(
    repo,
    torch_dtype=torch.bfloat16,
    device_map="auto",
).eval()

prompt = 'Email body: """Your account will be suspended unless you verify now."""\n\nTask: Is this phishing or safe? Reply with only one word: phishing or safe.'
inputs = tok.apply_chat_template(
    [{"role": "user", "content": prompt}],
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

with torch.no_grad():
    out = model.generate(**inputs, max_new_tokens=2, do_sample=False)

txt = tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True).strip().lower()
pred = "phishing" if "phishing" in txt else "safe"
print(pred, "| raw:", txt)

Merge Summary

Base model: google/gemma-3-4b-it
Adapter merged: briankkogi/gemma3-phishing-main-v1
Merged repo: briankkogi/gemma3-phishing-v1-bf16
Merge dtype: bf16

Metrics and Evaluation

For detailed training and evaluation results, see the source adapter model card: https://huggingface.co/briankkogi/gemma3-phishing-main-v1.

Limitations and Risks

False negatives are still possible.
Classifier outputs should be combined with other signals.
Evaluate on your own production-like distribution before rollout.
Prompt format/decoding settings can affect edge-case outputs.

Intended Use

Direct-serving inference endpoints
Security triage/copilot augmentation
Batch email risk scoring pipelines with human escalation

Out of Scope / Not Recommended

Sole automated enforcement without fallback controls
Compliance/legal final decisions without review
Use in high-risk environments without local re-validation and monitoring

License and Usage Terms

This merged model is derived from google/gemma-3-4b-it and is subject to upstream model terms and any dataset usage constraints. Review and comply with all applicable licenses/policies before deployment.

Version

v1-bf16

Downloads last month: 18

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for briankkogi/gemma3-phishing-v1-bf16

Base model

google/gemma-3-4b-pt

Finetuned

google/gemma-3-4b-it

Finetuned

(749)

this model