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Model: mwitiderrick/SwahiliInstruct-v0.2
Source: Original Platform

README.md

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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|
+