Instructions to use vete-speis/gemma-4-E2B-it-phishing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vete-speis/gemma-4-E2B-it-phishing with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-E2B-it")
model = PeftModel.from_pretrained(base_model, "vete-speis/gemma-4-E2B-it-phishing")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use vete-speis/gemma-4-E2B-it-phishing with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vete-speis/gemma-4-E2B-it-phishing:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vete-speis/gemma-4-E2B-it-phishing:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vete-speis/gemma-4-E2B-it-phishing:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vete-speis/gemma-4-E2B-it-phishing:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf vete-speis/gemma-4-E2B-it-phishing:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf vete-speis/gemma-4-E2B-it-phishing:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf vete-speis/gemma-4-E2B-it-phishing:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vete-speis/gemma-4-E2B-it-phishing:Q4_K_M

Use Docker

docker model run hf.co/vete-speis/gemma-4-E2B-it-phishing:Q4_K_M

LM Studio
Jan

vLLM

How to use vete-speis/gemma-4-E2B-it-phishing with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vete-speis/gemma-4-E2B-it-phishing"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vete-speis/gemma-4-E2B-it-phishing",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vete-speis/gemma-4-E2B-it-phishing:Q4_K_M

Ollama
How to use vete-speis/gemma-4-E2B-it-phishing with Ollama:
```
ollama run hf.co/vete-speis/gemma-4-E2B-it-phishing:Q4_K_M
```

Unsloth Studio

How to use vete-speis/gemma-4-E2B-it-phishing with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vete-speis/gemma-4-E2B-it-phishing to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vete-speis/gemma-4-E2B-it-phishing to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vete-speis/gemma-4-E2B-it-phishing to start chatting

How to use vete-speis/gemma-4-E2B-it-phishing with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vete-speis/gemma-4-E2B-it-phishing:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "vete-speis/gemma-4-E2B-it-phishing:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use vete-speis/gemma-4-E2B-it-phishing with Docker Model Runner:
```
docker model run hf.co/vete-speis/gemma-4-E2B-it-phishing:Q4_K_M
```

Lemonade

How to use vete-speis/gemma-4-E2B-it-phishing with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vete-speis/gemma-4-E2B-it-phishing:Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-E2B-it-phishing-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use vete-speis/gemma-4-E2B-it-phishing with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vete-speis/gemma-4-E2B-it-phishing:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default vete-speis/gemma-4-E2B-it-phishing:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use vete-speis/gemma-4-E2B-it-phishing with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vete-speis/gemma-4-E2B-it-phishing:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "vete-speis/gemma-4-E2B-it-phishing:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

vete-speis commited on Jun 10

Commit

7609647

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1 Parent(s): f94ec5a

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@@ -1,209 +1,87 @@
 ---
 base_model: google/gemma-4-E2B-it
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:google/gemma-4-E2B-it
 - 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]
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-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
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-## Uses
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-### Direct Use
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-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
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-### 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]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-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).
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-### Framework versions
-- PEFT 0.19.1

 ---
+license: gemma
 base_model: google/gemma-4-E2B-it
 library_name: peft
 pipeline_tag: text-generation
+language:
+- en
 tags:
+- phishing
+- email-classification
+- security
 - lora
+- gguf
+- gemma4
+datasets:
+- zefang-liu/phishing-email-dataset
 ---
+# Gemma-4-E2B-it — Phishing Email Classifier (LoRA)
+LoRA fine-tune of [google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it) that classifies emails as **`phishing`** or **`legit`** with a single-word answer.
+This repo contains:
+- the **LoRA adapter** (PEFT, ~50 MB) — reproducible, composable
+- ready-to-run **GGUF** quants (merged model) for llama.cpp / LM Studio: `Q4_K_M` (3.4 GB) and `Q8_0` (5 GB)
+## Results
+Evaluated on 1,000 held-out emails (balanced, English):
+| Model | Accuracy | Precision | Recall | F1 |
+|---|---|---|---|---|
+| gemma-4-E2B-it zero-shot | 85.7% | 90.3% | 78.9% | 0.842 |
+| **+ this LoRA** | **95.6%** | **98.4%** | **98.0%** | **0.982** |
+Notes: 26/1000 outputs were off-format and counted as errors (accuracy on parsable outputs: 98.2%). Zero-shot baseline measured on a 300-email subset.
+## Usage
+The model expects this exact system prompt (it was trained with it):
+```
+You are an email security classifier. Classify the email as 'phishing' or 'legit'. Respond with one word only.
+```
+### LM Studio / llama.cpp (GGUF)
+Download `phishing-gemma4-e2b-Q4_K_M.gguf`, set the system prompt above, temperature 0, paste an email as the user message. Requires a llama.cpp build recent enough for the `gemma4` architecture.
+### Transformers + PEFT (adapter)
+```python
+import torch
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+BASE = "google/gemma-4-E2B-it"
+tok = AutoTokenizer.from_pretrained(BASE)
+model = AutoModelForCausalLM.from_pretrained(BASE, dtype=torch.bfloat16, device_map="auto")
+model = PeftModel.from_pretrained(model, "vete-speis/gemma-4-E2B-it-phishing")
+msgs = [
+    {"role": "system", "content": "You are an email security classifier. Classify the email as 'phishing' or 'legit'. Respond with one word only."},
+    {"role": "user", "content": "Your account has been suspended. Verify now: http://secure-login.example.xyz"},
+]
+ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
+out = model.generate(ids, max_new_tokens=5, do_sample=False)
+print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))  # -> phishing
+```
+## Training
+- **Base:** google/gemma-4-E2B-it (2.3B effective params)
+- **Data:** [zefang-liu/phishing-email-dataset](https://huggingface.co/datasets/zefang-liu/phishing-email-dataset), cleaned, class-balanced 50/50, 10.5k train / 500 val / 1,000 test, emails truncated to 1,500 chars
+- **Method:** SFT with TRL `SFTTrainer` + PEFT LoRA — r=16, alpha=32, dropout 0.05, targets: the inner `nn.Linear` of q/k/v/o projections (Gemma 4 wraps them in `Gemma4ClippableLinear`, which PEFT cannot wrap directly)
+- **Run:** 1 epoch, effective batch 16 (4×4), seq len 512, lr 2e-4 cosine, bf16, gradient checkpointing — ~15 min on a single H100 (Modal)
+## Limitations
+- Trained on **English** emails. It generalizes surprisingly well to other languages (the base is multilingual), but no formal eval outside English.
+- The dataset's "phishing" class includes generic spam, so the model leans toward flagging unsolicited marketing/cold-outreach emails as `phishing`. If you need a strict spam ≠ phishing distinction, you need 3-class data.
+- ~2.6% of outputs may be off-format; parse with a contains-check on `phishing`/`legit` and treat anything else as abstention.
+- Not a substitute for a mail security gateway. Use as a triage aid.
+## License
+Gemma derivatives are governed by the [Gemma Terms of Use](https://ai.google.dev/gemma/terms). The adapter and GGUF files here are derivatives of google/gemma-4-E2B-it.