datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Instella-Long
Instella-Long
The Instella-Long dataset is a collection of pre-training and instruction following data that is used to train Instella-3B-Long-Instruct. The pre-training data is sourced from Prolong. For the SFT data, we use public datasets: Ultrachat 200K, OpenMathinstruct-2, Tülu-3 Instruction Following, and MMLU auxiliary train set. In addition, we generate synthetic long instruction data using documents of the books and arxiv from our pre-training corpus and the dclm subset from… See the full description on the dataset page: https://huggingface.co/datasets/amd/Instella-Long.Instella-GSM8K-synthetic
Instella-GSM8K-synthetic
The Instella-GSM8K-synthetic dataset was used in the second stage pre-training of Instella-3B model, which was trained on top of the Instella-3B-Stage1 model.
This synthetic dataset was generated using the training set of GSM8k dataset, where we first used Qwen2.5-72B-Instruct to
Abstract numerical values as function parameters and generate a Python program to solve the math question.
Identify and replace numerical values in the existing question with… See the full description on the dataset page: https://huggingface.co/datasets/amd/Instella-GSM8K-synthetic.Instella-Reasoninginstella-3b-blind-spots
Instella-3B Blind Spots
Model tested
Model: amd/Instella-3Bhttps://huggingface.co/amd/Instella-3B
How I loaded the model (Colab)
!pip -q install -U transformers accelerate datasets huggingface_hub bitsandbytes
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
MODEL_ID = "amd/Instella-3B"
use_cuda = torch.cuda.is_available()
dtype = torch.bfloat16 if (use_cuda and torch.cuda.is_bf16_supported()) else torch.float16… See the full description on the dataset page: https://huggingface.co/datasets/mdasad7/instella-3b-blind-spots.