Instructions to use Janeodum/tsaro-e2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Janeodum/tsaro-e2b with Transformers:

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

pipe = pipeline("text-generation", model="Janeodum/tsaro-e2b")
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-e2b")
model = AutoModelForImageTextToText.from_pretrained("Janeodum/tsaro-e2b")
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-e2b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Janeodum/tsaro-e2b"
# 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-e2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Janeodum/tsaro-e2b

SGLang

How to use Janeodum/tsaro-e2b 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-e2b" \
    --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-e2b",
		"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-e2b" \
        --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-e2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Janeodum/tsaro-e2b with Docker Model Runner:
```
docker model run hf.co/Janeodum/tsaro-e2b
```

tsaro-e2b / README.md

Janeodum

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	---
	license: gemma
	base_model: google/gemma-4-E2B-it
	base_model_relation: finetune
	library_name: transformers
	tags:
	- gemma-4
	- tsaro
	- threat-extraction
	language:
	- ha
	- en
	pipeline_tag: text-generation
	---

	# Tsaro Gemma 4 E2B

	Fine-tuned Gemma 4 E2B 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-e2b-it`](https://huggingface.co/google/gemma-4-e2b-it)
	- Fine-tuning: LoRA adapter trained on Tsaro threat-report data, then merged
	into the base weights
	- Role in Tsaro: the E2B variant is the smaller of two on-device extraction
	models, used as the fallback for older or low-RAM Android devices

	## Derived models

	- [`Janeodum/tsaro-e2b-gguf`](https://huggingface.co/Janeodum/tsaro-e2b-gguf) — GGUF quantization for
	on-device inference via llama.cpp / llama.rn

	## Training data

	Fine-tuned on threat-report examples spanning Hausa, Pidgin, and English,
	including examples derived from the ACLED Nigeria conflict archive 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.