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---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- transformers
datasets:
- mwitiderrick/SwahiliAlpaca
base_model: mistralai/Mistral-7B-Instruct-v0.2
inference: true
model_type: mistral
created_by: mwitiderrick
pipeline_tag: text-generation
model-index:
- name: SwahiliInstruct-v0.2
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 55.2
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/SwahiliInstruct-v0.2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 78.22
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/SwahiliInstruct-v0.2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 50.3
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/SwahiliInstruct-v0.2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 57.08
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/SwahiliInstruct-v0.2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 73.24
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/SwahiliInstruct-v0.2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 11.45
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/SwahiliInstruct-v0.2
name: Open LLM Leaderboard
---
# SwahiliInstruct-v0.2
This is a [Mistral model](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) that has been fine-tuned on the [Swahili Alpaca dataset](https://huggingface.co/datasets/mwitiderrick/SwahiliAlpaca) for 3 epochs.
## Prompt Template
```
### Maelekezo:
{query}
### Jibu:
<Leave new line for model to respond>
```
## Usage
```python
# 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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_mwitiderrick__SwahiliInstruct-v0.2)
| 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|
|