bshada/infinity-hw-mistral-finetune-8192 dataset hosted on Hugging Face and contributed by the HF Datasets community

deveshparagiri/dataset-Mistral-7B-v0.1-finetune-20250716-151443 dataset hosted on Hugging Face and contributed by the HF Datasets community

jag2023/mistral-fine-tune-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

A classics data set for use with mistral-7b-v0.1

This dataset was used for a fine-tune of Mistral 7b base model. It contains 1,640 Q/A pairs on Greek & Roman history. The dataset was generated via Mixtral-8x7b Instruct v01, run over 512 token-length chunks of vol's 2&3 of Will Durants' 13 vol Story of Civilization (Life of Greece and Caesar & Christ). Training data was formatted with [INST] and [/INST] delimiting instructions: {"text": "Q: "Why did many Greeks come to resent Rome's… See the full description on the dataset page: https://huggingface.co/datasets/wmmarcellino/mistral-7b-v0.1-GreeceRome-v0.1.

Description

This dataset is a modified version of Locutusque/function-calling-chatml where the messages were obtained by applying the scripts in mistral-finetune function calling guide. Only the messages that contain tool entries have been kept, so this dataset could be used for SFT following the Mistral Finetune guide.

  Example

{ "system_message": "You are a helpful assistant with access to the following functions. Use them if required -"… See the full description on the dataset page: https://huggingface.co/datasets/plaguss/function_calling_finetune_mistral.

Explanation

A small experiment insipred by the Mistral playing DOOM experiment from the Mistral Hackathon How it works? Audio -> Waveform Visualization -> Waveform ASCII Art -> Finetune Mistral on ASCII Art to predict text from ASCII Art

Quick video explanation

  Example Waveform







  Example ASCII Art… See the full description on the dataset page: https://huggingface.co/datasets/0-hero/lj_speech_with_spectogram_conversations.

Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!

We have a free Google Colab Tesla T4 notebook for Mistral Nemo 12b here: https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing

  ✨ Finetune for Free

All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.

Unsloth supports Free Notebooks… See the full description on the dataset page: https://huggingface.co/datasets/NewEden/Nemo-Base-V7-Tekken.

putawararevalda/mistral-finetune-instruct-digistar-telkom dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset made for instruction supervised finetuning of Mistral LLMs, by combining of medical datasets and getting 2k entries from them:

Medical meadow wikidoc (https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc/blob/main/README.md) Medquad (https://www.kaggle.com/datasets/jpmiller/layoutlm)

  Medical meadow wikidoc

The Medical Meadow Wikidoc dataset comprises question-answer pairs sourced from WikiDoc, an online platform where medical professionals collaboratively… See the full description on the dataset page: https://huggingface.co/datasets/Shekswess/medical_mistral_instruct_dataset_short.

Description

A small subset of dataset b-mc2/sql-create-context, used to fine-tune yulingai12/Mistral-7B-Instruct-v0.2-SQL-finetuned-test

aLLoyM Training Dataset

This dataset was used to fine-tune the aLLoyM model (Mistral-based).

  Dataset Statistics

Total Examples: 2,035,791 Average Instruction Length: 113.1 characters Average Question Length: 76.4 characters
Average Answer Length: 33.9 characters Total Dataset Size: 668.55 MB

  Format

The dataset is in JSONL format where each line contains: { "messages": [ {"role": "system", "content": "System instruction"}, {"role": "user"… See the full description on the dataset page: https://huggingface.co/datasets/Playingyoyo/aLLoyM-dataset.

Bitext - Travel Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [Travel] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An overview of… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-travel-llm-chatbot-training-dataset.

Raw responses generated by Phi4 , questions from alamios/Mistral-Small-24B-Instruct-2501-Conversations Made it to use on the QwenPhi 0.5B Draft model, but the finetune did not yield much improvement, still I have generated the dataset so here is the raw data hopefully it is useful for someone.

Bitext - Restaurants Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [restaurants] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-restaurants-llm-chatbot-training-dataset.