datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
EagleX-WorldContinued
Dataset Card for EagleX v2 Dataset
This dataset was used to train RWKV Eagle 7B for continued pretrain of 1.1T tokens (approximately) (boosting it to 2.25T) with the final model being released as RWKV EagleX v2.
Dataset Details
Dataset Description
EagleX-WorldContinued is a pretraining dataset built from many of our datasets over at Recursal AI + a few others.
Curated by: M8than, KaraKaraWitch, Darok
Funded by [optional]: Recursal.ai
Shared by [optional]:… See the full description on the dataset page: https://huggingface.co/datasets/RWKV/EagleX-WorldContinued.EagleSFT
Dataset Card for 🦅 EagleSFT
Dataset Summary
This dataset contains 536,231 pairs of human questions and machine-generated responses intended for supervised fine-tuning (SFT) of large language models. The dataset includes both Russian and English content, with linked IDs allowing for cross-lingual analysis. It was created by processing an initial collection of 739,732 human questions posed to LLMs, predominantly in Russian (about 99%) with a small portion in English (about… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/EagleSFT.EAGLE3-Apertus-8B-Instruct-2509-Data
EAGLE3-Apertus-8B-Instruct-2509-Data
Training dataset for the thomaskiefer/EAGLE3-Apertus-8B-Instruct-2509 speculative decoding draft model.
Dataset Description
This dataset contains ~375k multi-turn conversations used to train an Eagle3 draft model for swiss-ai/Apertus-8B-Instruct-2509.
Data Sources
The prompts are sourced from:
UltraChat - Large-scale multi-turn dialogue dataset
ShareGPT - Real user conversations
OpenThoughts-114k-math - Mathematical… See the full description on the dataset page: https://huggingface.co/datasets/thomaskiefer/EAGLE3-Apertus-8B-Instruct-2509-Data.MoS-Qwen3-8B-EAGLE3-responses
MoS — Qwen3-8B EAGLE3 Training Responses
Target-model responses for training EAGLE3 speculative-decoding draft models against
Qwen/Qwen3-8B. Built for the MoS (Mixture of
Speculators) project — a routed multi-MLP draft — and equally usable for any single-draft
EAGLE3 / SpecForge training run on Qwen3-8B.
599,087 complete assistant responses (with thinking traces) over five domains, generated
by Qwen3-8B itself so the draft learns to mimic the target's own distribution.… See the full description on the dataset page: https://huggingface.co/datasets/ryan-0608/MoS-Qwen3-8B-EAGLE3-responses.Tobacco-Expert-Dataseteagle3-sarvam-30b-training-data
Eagle3 Sarvam-30B Training Data
Training data used to build the Eagle3 draft model for Sarvam-30B.
Dataset Description
This dataset contains 90,000 prompt-response pairs used to train an Eagle3 speculative decoding draft model for the Sarvam-30B language model.
Each sample consists of a prompt and its corresponding response generated by the Sarvam-30B base model. During training, the model also consumes hidden state features extracted from auxiliary layers of the base… See the full description on the dataset page: https://huggingface.co/datasets/sulabhkatiyar/eagle3-sarvam-30b-training-data.Tobacco-Expert-Dataset2