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

Libraries

How to use Janeodum/tsaro-e4b with Transformers:

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

pipe = pipeline("text-generation", model="Janeodum/tsaro-e4b")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Janeodum/tsaro-e4b")
model = AutoModelForImageTextToText.from_pretrained("Janeodum/tsaro-e4b")
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

vLLM

How to use Janeodum/tsaro-e4b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Janeodum/tsaro-e4b

SGLang

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

Docker Model Runner
How to use Janeodum/tsaro-e4b with Docker Model Runner:
```
docker model run hf.co/Janeodum/tsaro-e4b
```

tsaro-e4b / README.md

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metadata

license: gemma
base_model: google/gemma-4-E4B-it
base_model_relation: finetune
library_name: transformers
tags:
  - gemma-4
  - tsaro
  - threat-extraction
language:
  - ha
  - en
pipeline_tag: text-generation

Tsaro Gemma 4 E4B

Fine-tuned Gemma 4 E4B threat extraction model for Tsaro, a shared safety system for Northern Nigeria.

What this model does

Given an unstructured report in Hausa, Pidgin, or English, this model returns a structured threat signal — threat type, location, perpetrator and vehicle counts, direction of movement, time references, and a confidence score — and judges whether the message is a genuine security report at all.

Model details

Base model: google/gemma-4-E4B-it
Fine-tuning: LoRA adapter trained on Tsaro threat-report data, then merged into the base weights
Role in Tsaro: E4B is the primary on-device extraction model — the default on any reasonably modern Android device. The Tsaro app loads the largest model the hardware can run, falling back from E4B to E2B to a hosted endpoint.

Training data

Fine-tuned on 35,512 examples spanning Hausa, Pidgin, and English: 2,500 synthetic threat reports plus 33,262 examples derived from the ACLED Nigeria conflict archive, each paired with Hausa and Pidgin translations.

Intended use and limitations

Built for community safety reporting in a specific regional context. Not a general-purpose model. Outputs are extraction assistance, not verified intelligence.