Instructions to use Sunbird/Sunflower-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sunbird/Sunflower-14B with Transformers:

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

pipe = pipeline("text-generation", model="Sunbird/Sunflower-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sunbird/Sunflower-14B")
model = AutoModelForCausalLM.from_pretrained("Sunbird/Sunflower-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Sunbird/Sunflower-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sunbird/Sunflower-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sunbird/Sunflower-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Sunbird/Sunflower-14B

SGLang

How to use Sunbird/Sunflower-14B 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 "Sunbird/Sunflower-14B" \
    --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": "Sunbird/Sunflower-14B",
		"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 "Sunbird/Sunflower-14B" \
        --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": "Sunbird/Sunflower-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Sunbird/Sunflower-14B with Docker Model Runner:
```
docker model run hf.co/Sunbird/Sunflower-14B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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🌻 Sunflower-14B Model Card

Model Description

🌻 Sunflower-14B is a multilingual language model developed by Sunbird AI for Ugandan languages. Built on the Qwen 3-14B architecture, the model supports translation and text generation across 31 Ugandan languages plus English.

The latest version incorporates a GRPO fine-tuned checkpoint that significantly improves:

Translation accuracy and fluency
Factual question-answering
Instruction following
Response consistency and reasoning quality
Reduced hallucinations compared to earlier releases

The updated model is especially stronger at multilingual factual Q&A, cross-lingual understanding, and producing more natural translations across low-resource Ugandan languages.

Developed by: Sunbird AI
Model type: Causal language model
Base architecture: Qwen3-14B
Languages: 31 Ugandan languages + English (see language codes above)

What's New in This Release

🚀 GRPO Fine-Tuned Version

This release integrates a GRPO (Group Relative Preference Optimization) fine-tuning stage through the checkpoint:

The GRPO optimization focused on:

Improving factual correctness
Better multilingual alignment
More reliable instruction following
Reducing repetitive or inconsistent outputs
Improving answer grounding and coherence

Compared to the previous version, this release provides:

More accurate translations
Better conversational quality
Stronger factual Q&A performance
Improved multilingual reasoning

Intended Uses

Primary Use Cases

Translation between English and Ugandan languages
Translation between Ugandan languages
Text generation in Ugandan languages
Multilingual factual question answering
Educational and knowledge-access applications
Summarization and explanation tasks

Example Usage

import transformers
import torch

MODEL_PATH = "Sunbird/Sunflower-14B"

SYSTEM_MESSAGE = (
    "You are Sunflower, a multilingual assistant made by Sunbird AI "
    "who understands Ugandan languages and English. "
    "You specialise in accurate translations, factual question answering, "
    "summaries, explanations, and multilingual reasoning."
)

tokenizer = transformers.AutoTokenizer.from_pretrained(MODEL_PATH)

model = transformers.AutoModelForCausalLM.from_pretrained(
    MODEL_PATH,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

instruction = "Translate from Luganda to English: Wano webawaaba?"

messages = [
    {"role": "system", "content": SYSTEM_MESSAGE},
    {"role": "user", "content": instruction}
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

inputs = tokenizer([prompt], return_tensors="pt").to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=500,
    temperature=0.5,
    top_p=0.9,
    do_sample=True,
)

response = tokenizer.decode(
    outputs[0][len(inputs["input_ids"][0]):],
    skip_special_tokens=True
)

print(response)

API Access

For production use, the model is available through the Sunbird AI API:

https://api.sunbird.ai/

Training Details

Training Data

The model was trained on approximately 750 million characters of multilingual text collected from:

Digitized books and educational materials
Radio transcripts (500+ hours transcribed)
Web data from MADLAD-400 and Common Crawl
Existing multilingual datasets (SALT, FLORES-200, MT560, TICO-19)
Dictionaries, proverbs, and cultural documents

Training Procedure

Stage 1: Continued Pretraining

Base model: Qwen3-14B
Objective: Next-token prediction
Infrastructure: 4× H200 GPUs
Configuration: DeepSpeed ZeRO-3

Stage 2: Supervised Fine-Tuning (SFT)

Dataset: Instruction-response multilingual datasets
Tasks:
- Translation
- Summarization
- Question answering
- Instruction following

Stage 3: Preference Optimization

Initial alignment with preference optimization methods
Reduced hallucinations and glitching behavior

Stage 4: GRPO Fine-Tuning (New)

Checkpoint: jq/sunflower-14b-grpo-combined
Objective:
- Improve factual correctness
- Improve multilingual reasoning
- Improve answer quality and consistency
- Better conversational alignment
Outcome:
- More accurate factual responses
- Better translation fluency
- Stronger instruction adherence
- Improved multilingual Q&A quality

Evaluation

Translation Performance

The model was evaluated on a multilingual benchmark dataset covering practical scenarios including:

Healthcare
Agriculture
Education
Banking
Government services

Average scores across 31 languages:

chrF (xx→eng): 0.419
chrF (eng→xx): 0.366
BLEU (xx→eng): 19.61

The GRPO fine-tuned release additionally improves:

Translation fluency
Context preservation
Answer consistency
Factual reliability

Comparison with Previous Release

Capability	Previous Release	GRPO Release
Translation Accuracy	Strong	Improved
Factual Q&A	Moderate	Strong
Instruction Following	Good	Improved
Hallucination Reduction	Partial	Better
Response Consistency	Moderate	Improved
Multilingual Reasoning	Good	Stronger

Comparison with Other Models

Model	chrF (xx→eng)	chrF (eng→xx)
Sunflower-14B	0.419	0.366
Gemini 2.5 Pro	0.408	0.301
GPT-4o	0.354	0.235

Limitations

Performance varies across languages depending on available training data
Some low-resource languages may still produce inconsistent outputs
The model may still hallucinate in highly specialized domains
Not intended for fully autonomous high-risk decision making
Human review is recommended for medical, legal, or financial use cases

Bias and Ethical Considerations

The model was trained on multilingual web and cultural datasets which may contain historical or social biases. Users should:

Review outputs in critical applications
Validate sensitive translations with native speakers
Be aware that low-resource language generation remains an active research area

Citation

@misc{sunflower2025,
  title={Sunflower: A Regional Approach to Large Language Models for Ugandan Languages},
  author={Akera, Benjamin and Nafula, Evelyn and Yiga, Gilbert and Natukunda, Phionah and Nsumba, Solomon and Muhanguzi, Joel and Namara, Janat and Sekalala, Imran and Walukagga, Patrick and Bainomugisha, Engineer and Mwebaze, Ernest and Quinn, John},
  year={2025},
  publisher={Sunbird AI}
}