Instructions to use africatic/atic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use africatic/atic with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-E4B-it")
model = PeftModel.from_pretrained(base_model, "africatic/atic")

Transformers

How to use africatic/atic with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("africatic/atic", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use africatic/atic with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/africatic/atic

SGLang

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

Docker Model Runner
How to use africatic/atic with Docker Model Runner:
```
docker model run hf.co/africatic/atic
```

atic / README.md

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metadata

base_model: google/gemma-4-E4B-it
library_name: peft
pipeline_tag: text-generation
license: apache-2.0
datasets:
  - africatic/afritemp-bench
tags:
  - base_model:adapter:google/gemma-4-E4B-it
  - lora
  - sft
  - transformers
  - trl

Gemma 4 E4B — AfriTemp LoRA

This repository contains a LoRA adapter fine-tuned from google/gemma-4-E4B-it on africatic/afritemp-bench.

Training setup

Method: LoRA supervised fine-tuning
Precision: BF16
GPU: NVIDIA A100-SXM4-40GB
Maximum sequence length: 1024
Epochs: 3
Per-device batch size: 1
Gradient accumulation: 16
Effective batch size: 16
Learning rate: 0.0002
LoRA rank: 16
LoRA alpha: 32

Intended use

The adapter is intended for research on temporal reasoning and African economic, social and development data. It should be evaluated carefully before use in consequential decision-making.

Loading

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model_id = "google/gemma-4-E4B-it"
adapter_id = "YOUR_HF_USERNAME/gemma-4-e4b-afritemp"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(base_model, adapter_id)

Framework versions

PEFT 0.19.1