SwahiliInstruct-v0.2
This is a Mistral model that has been fine-tuned on the Swahili Alpaca dataset for 3 epochs.
Prompt Template
### Maelekezo:
{query}
### Jibu:
<Leave new line for model to respond>
Usage
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/SwahiliInstruct-v0.2")
model = AutoModelForCausalLM.from_pretrained("mwitiderrick/SwahiliInstruct-v0.2", device_map="auto")
query = "Nipe maagizo ya kutengeneza mkate wa mandizi"
text_gen = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200, do_sample=True, repetition_penalty=1.1)
output = text_gen(f"### Maelekezo:\n{query}\n### Jibu:\n")
print(output[0]['generated_text'])
"""
Maagizo ya kutengeneza mkate wa mandazi:
1. Preheat tanuri hadi 375°F (190°C).
2. Paka sufuria ya uso na siagi au jotoa sufuria.
3. Katika bakuli la chumvi, ongeza viungo vifuatavyo: unga, sukari ya kahawa, chumvi, mdalasini, na unga wa kakao.
Koroga mchanganyiko pamoja na mbegu za kikombe 1 1/2 za mtindi wenye jamii na hatua ya maji nyepesi.
4. Kando ya uwanja, changanya zaini ya yai 2
"""
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 54.25 |
| AI2 Reasoning Challenge (25-Shot) | 55.20 |
| HellaSwag (10-Shot) | 78.22 |
| MMLU (5-Shot) | 50.30 |
| TruthfulQA (0-shot) | 57.08 |
| Winogrande (5-shot) | 73.24 |
| GSM8k (5-shot) | 11.45 |