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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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
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 — 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.

Downloads last month: 113

Safetensors

Model size

5B params

Tensor type

BF16

Model tree for Janeodum/tsaro-e2b

Base model

google/gemma-4-E2B

Finetuned

google/gemma-4-E2B-it

Finetuned

(161)

this model

Quantizations

1 model