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