Radu1999

Adding Evaluation Results (#1)

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	---
	license: apache-2.0
	library_name: transformers
	model-index:
	- name: Mistral-Instruct-Ukrainian-SFT
	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: 57.85
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Radu1999/Mistral-Instruct-Ukrainian-SFT
	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: 83.12
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Radu1999/Mistral-Instruct-Ukrainian-SFT
	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: 60.95
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Radu1999/Mistral-Instruct-Ukrainian-SFT
	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: 54.14
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Radu1999/Mistral-Instruct-Ukrainian-SFT
	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: 77.51
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Radu1999/Mistral-Instruct-Ukrainian-SFT
	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: 39.42
	name: accuracy
	source:
	url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Radu1999/Mistral-Instruct-Ukrainian-SFT
	name: Open LLM Leaderboard
	---

	# Model card for Mistral-Instruct-Ukrainian-SFT

	Supervised finetuning of Mistral-7B-Instruct-v0.2 on Ukrainian datasets.


	## Instruction format

	In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens.

	E.g.
	```
	text = "[INST]Відповідайте лише буквою правильної відповіді: Елементи експресіонізму наявні у творі: A. «Камінний хрест», B. «Інститутка», C. «Маруся», D. «Людина»[/INST]"
	```

	This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:

	## Model Architecture
	This instruction model is based on Mistral-7B-v0.2, a transformer model with the following architecture choices:
	- Grouped-Query Attention
	- Sliding-Window Attention
	- Byte-fallback BPE tokenizer

	## Datasets
	- [UA-SQUAD](https://huggingface.co/datasets/FIdo-AI/ua-squad/resolve/main/ua_squad_dataset.json)
	- [Ukrainian StackExchange](https://huggingface.co/datasets/zeusfsx/ukrainian-stackexchange)
	- [UAlpaca Dataset](https://github.com/robinhad/kruk/blob/main/data/cc-by-nc/alpaca_data_translated.json)
	- [Ukrainian Subset from Belebele Dataset](https://github.com/facebookresearch/belebele)
	- [Ukrainian Subset from XQA](https://github.com/thunlp/XQA)

	## 💻 Usage

	```python
	!pip install -qU transformers accelerate

	from transformers import AutoTokenizer
	import transformers
	import torch

	model = "Radu1999/Mistral-Instruct-Ukrainian-SFT"
	messages = [{"role": "user", "content": "What is a large language model?"}]

	tokenizer = AutoTokenizer.from_pretrained(model)
	prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	pipeline = transformers.pipeline(
	"text-generation",
	model=model,
	torch_dtype=torch.bfloat16,
	device_map="auto",
	)

	outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
	print(outputs[0]["generated_text"])
	```

	## Author

	Radu Chivereanu
	# [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_Radu1999__Mistral-Instruct-Ukrainian-SFT)

	\| Metric \|Value\|
	\|---------------------------------\|----:\|
	\|Avg. \|62.17\|
	\|AI2 Reasoning Challenge (25-Shot)\|57.85\|
	\|HellaSwag (10-Shot) \|83.12\|
	\|MMLU (5-Shot) \|60.95\|
	\|TruthfulQA (0-shot) \|54.14\|
	\|Winogrande (5-shot) \|77.51\|
	\|GSM8k (5-shot) \|39.42\|