SwahiliInstruct-v0.2/README.md

---
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|