Instructions to use Japhari/sdt-gm4-hesabu-flare-v0.01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Japhari/sdt-gm4-hesabu-flare-v0.01 with Transformers:

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

pipe = pipeline("text-generation", model="Japhari/sdt-gm4-hesabu-flare-v0.01")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Japhari/sdt-gm4-hesabu-flare-v0.01", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Japhari/sdt-gm4-hesabu-flare-v0.01 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Japhari/sdt-gm4-hesabu-flare-v0.01"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Japhari/sdt-gm4-hesabu-flare-v0.01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Japhari/sdt-gm4-hesabu-flare-v0.01

SGLang

How to use Japhari/sdt-gm4-hesabu-flare-v0.01 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 "Japhari/sdt-gm4-hesabu-flare-v0.01" \
    --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": "Japhari/sdt-gm4-hesabu-flare-v0.01",
		"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 "Japhari/sdt-gm4-hesabu-flare-v0.01" \
        --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": "Japhari/sdt-gm4-hesabu-flare-v0.01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Japhari/sdt-gm4-hesabu-flare-v0.01 with Docker Model Runner:
```
docker model run hf.co/Japhari/sdt-gm4-hesabu-flare-v0.01
```

sdt-gm4-hesabu-flare-v0.01

Swahili math-assistant LoRA adapter focused on primary-school arithmetic and word problems.

Model details

Model name: sdt-gm4-hesabu-flare-v0.01
Base model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
Fine-tuning approach: QLoRA / LoRA adapter fine-tuning
Primary language: Swahili
Target domain: School-level mathematics tutoring

Training data

This model was trained with supervised chat-style examples from:

SFT train/validation splits (train.jsonl, val.jsonl)
Arithmetic booster set (math_booster.jsonl)

Associated dataset repo:

Japhari/sdt-gm4-hesabu-flare-v0.01-dataset

Intended use

Swahili math Q&A assistance
Educational demos and experimentation
Baseline model for further instruction tuning

Limitations

May still hallucinate on complex multi-step reasoning
Performance degrades on prompts missing key quantities
Not suitable as sole source for high-stakes grading/financial decisions

Safety notes

For production or assessment workflows, use deterministic validation/guardrails for arithmetic final answers.

Inference example (Transformers + PEFT)

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = "unsloth/gemma-3-4b-it-unsloth-bnb-4bit"
adapter_repo = "Japhari/sdt-gm4-hesabu-flare-v0.01"

tokenizer = AutoTokenizer.from_pretrained(adapter_repo)
model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto")
model = PeftModel.from_pretrained(model, adapter_repo)

prompt = "Mara 8 kwa 7 ni ngapi?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(out[0], skip_special_tokens=True))

Version

v0.01 – initial release

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for Japhari/sdt-gm4-hesabu-flare-v0.01

Base model

google/gemma-3-4b-pt

Finetuned

google/gemma-3-4b-it

Quantized

unsloth/gemma-3-4b-it-unsloth-bnb-4bit

Adapter

(128)

this model

Japhari
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sdt-gm4-hesabu-flare-v0.01