Instructions to use cyberandy/Alpino-e4b-v01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use cyberandy/Alpino-e4b-v01 with PEFT:
```
Task type is invalid.
```

How to use cyberandy/Alpino-e4b-v01 with Transformers:

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

pipe = pipeline("text-generation", model="cyberandy/Alpino-e4b-v01")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("cyberandy/Alpino-e4b-v01", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cyberandy/Alpino-e4b-v01 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cyberandy/Alpino-e4b-v01"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cyberandy/Alpino-e4b-v01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cyberandy/Alpino-e4b-v01

SGLang

How to use cyberandy/Alpino-e4b-v01 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 "cyberandy/Alpino-e4b-v01" \
    --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": "cyberandy/Alpino-e4b-v01",
		"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 "cyberandy/Alpino-e4b-v01" \
        --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": "cyberandy/Alpino-e4b-v01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cyberandy/Alpino-e4b-v01 with Docker Model Runner:
```
docker model run hf.co/cyberandy/Alpino-e4b-v01
```

Configuration Parsing Warning:In adapter_config.json: "peft.task_type" must be a string

Alpino-e4b-v01 — Teaching Small Models the Governance Language of an Agentic Website

Alpino-e4b-v01 demonstrates that an edge-oriented, small multimodal model (google/gemma-4-E4B-it) can master the strict governance language, RDF/SHACL observation loop, and Agentic Observation-Orientation-Execution (AOOE) protocol required to operate an autonomous agentic storefront website (Alpina.travel), serving alpine travel planners and apartments in Lungau.

Without altering model weights, changing prompt definitions, or loosening schema rules, fine-tuning google/gemma-4-E4B-it on Alpina's governed webmaster curriculum elevated its protocol compliance from 0% (0/20 valid) on the untouched baseline to 100% (20/20 valid) on the post-SFT capability floor.

Lineage

graph LR
    A["Gemma 4 E4B (Base)"] --> B["Alpino v0.1 SFT"]
    B --> C["20/20 AOOE Capability Floor"]

Highlights & Core Methodology

Agentic Storefront Governance: Rather than relying on unconstrained natural language or vendor-specific tool syntax, Alpino-e4b-v01 speaks the exact domain governance language of the website. It reasons over RDF Knowledge Graphs and SHACL constraints, respects provider authority boundaries (e.g. availability), enforces Pull Request review flows, and refuses forbidden direct-state writes.
Small-Model Precision: Fine-tuning focuses exclusively on text projections (language_model LoRA targets, $r=16, \alpha=32$), keeping vision and audio towers 100% frozen. This allows lightweight edge deployment (mobile/edge intent) without losing multimodal capability.
Strict Protocol Alignment: The model transitions from outputting conversational prose or invalid markdown to generating deterministic AOOE XML envelopes containing <reasoning>, <action>, and <answer> blocks.

Benchmark & Capability Floor Results

Evaluated on the frozen AOOE 20-prompt capability floor (WEB-CF-51010..51029) in bfloat16 on an NVIDIA H100 GPU:

Model	Valid Transcripts	Format Rate	Threshold (95%) Status
Untouched Baseline (`google/gemma-4-E4B-it`)	0 / 20	0.0 (0%)	FAILED
Alpino-e4b-v01 (Post-SFT)	20 / 20	1.0 (100%)	PASSED

Key Improvements

100% Protocol Compliance: Eliminates free-text prose, markdown codeblocks, and model-native function call syntax in favor of exact AOOE XML tags (<action type="...">tool_name({args})</action>).
Grounding & Safety: Respects provider-owned availability boundaries, enforces Git-based review before publishing, and refuses unverified media or out-of-scope edits.

Model Details

Developed by: CyberAndy / Alpina.travel (cyberandy/alpina-travel)
Base Model: google/gemma-4-E4B-it (revision ee0ef6023621cff504d758262d4e04895a5af4a2)
Adapter Name: Alpino-e4b-v01 (gemma4-e4b-text-lora-v1)
Model Type: PEFT LoRA Adapter (Multimodal Text Projections)
Languages: English, German, Italian
License: Apache-2.0

Training Details

Dataset & Training Setup

Curriculum: 48 multi-turn governed SFT traces compiled into 147 next-assistant-turn training examples covering webmaster operations and content manager decision-making.
LoRA Parameters:
- Rank ($r$): 16
- Alpha ($\alpha$): 32
- Dropout: 0.05
- Target Modules: All linear projections under language_model (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj).
- Isolated Projections: Vision and audio towers remain 100% frozen (0 vision/audio trainable parameters).
Training Infrastructure: Modal H100 GPU (bfloat16 precision).
Hyperparameters:
- Epochs: 3.0
- Batch Size: 1 (gradient accumulation 8)
- Learning Rate: 2e-4
- Final Loss: 0.6961 (57 global steps)

Quickstart & Usage

import torch
from transformers import AutoModelForMultimodalLM, AutoProcessor
from peft import PeftModel

base_model_id = "google/gemma-4-E4B-it"
adapter_id = "cyberandy/Alpino-e4b-v01"

# Load processor and model
processor = AutoProcessor.from_pretrained(base_model_id)
model = AutoModelForMultimodalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Load Alpino-e4b-v01 LoRA adapter
model = PeftModel.from_pretrained(model, adapter_id)

messages = [
    {
        "role": "system",
        "content": "You are the Alpina KG-native webmaster. Execute instructions through the AOOE protocol."
    },
    {
        "role": "user",
        "content": "Replace the hero image for Mariapfarr with the newly verified media asset."
    }
]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    enable_thinking=False
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
response = processor.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
print(response)

Citation & Provenance

Alpina Source Repository: cyberandy/alpina-travel
Source Commit: 7cd62aecd20c178ad72f312845009836abcb22e7
Baseline Run ID: alpina-gemma4-e4b-baseline-20260813-7cd62aec
SFT Full Run ID: alpina-gemma4-e4b-sft-full-7ac1951a

APA:

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Glossary [optional]

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Model Card Authors [optional]

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Model Card Contact

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Framework versions

PEFT 0.19.1

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Model tree for cyberandy/Alpino-e4b-v01

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Adapter

(323)

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