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 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
- Ukrainian StackExchange
- UAlpaca Dataset
- Ukrainian Subset from Belebele Dataset
- Ukrainian Subset from XQA
💻 Usage
!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
Detailed results can be found here
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 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard57.850
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard83.120
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard60.950
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard54.140
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard77.510
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard39.420