agentic-pretraining
TR-HASH-Pretraining-125B-Agentic-32K
TR-HASH Pretraining 125B — Agentic 32K
Private, source-curated pretraining artifact for the TR-HASH Agentic 32K model
line. It contains 125B packed token exposures: 75B foundation and 50B
agentic/procedural content.
The corpus uses the immutable, validated 32,000-ID revision of
AETHORIA-AI/TR-HASH-Tokenizer-32K-Agentic. It is not compatible with the
older TR-HASH 32K tokenizer.
Composition
Bucket
Tokens
Purpose
Foundation
75B
English and French… See the full description on the dataset page: https://huggingface.co/datasets/AETHORIA-AI/TR-HASH-Pretraining-125B-Agentic-32K.agentic-llm-pretraining-1.7b
Agentic LLM Pretraining Dataset
A pretraining corpus for small language models (1-3B parameters) optimized for agentic tasks. The corpus emphasizes learning to comprehend language, reason, follow instructions, and use tools over memorizing factual knowledge — the assumption is that domain knowledge will be provided at runtime via RAG. The idea is that this could enable much smaller pretraining corpora by omitting the large volumes of text typically needed to memorize facts.… See the full description on the dataset page: https://huggingface.co/datasets/visionscaper/agentic-llm-pretraining-1.7b.agentic-llm-pretraining-1.7b-tokenized-qwen3-4k
Agentic LLM Pretraining Dataset - Tokenized (Qwen3, 4K context)
Pre-tokenized version of visionscaper/agentic-llm-pretraining-1.7b for pre-training small language models for agentic AI use cases.
Overview
Property
Value
Source dataset
visionscaper/agentic-llm-pretraining-1.7b
Tokenizer
Qwen/Qwen3-1.7B
Context length
4,096 tokens
EOD token
<|endoftext|> (ID 151643)
Token dtype
uint32
Total samples
375,384
Total tokens
~1.54 billion
Storage
~5.8 GB… See the full description on the dataset page: https://huggingface.co/datasets/visionscaper/agentic-llm-pretraining-1.7b-tokenized-qwen3-4k.agentic-backdoor-active-evalagentic-llm-pretraining-1.7b
Agentic LLM Pretraining Dataset
A pretraining corpus for small language models (1-3B parameters) optimized for agentic tasks. The corpus emphasizes learning to comprehend language, reason, follow instructions, and use tools over memorizing factual knowledge — the assumption is that domain knowledge will be provided at runtime via RAG. The idea is that this could enable much smaller pretraining corpora by omitting the large volumes of text typically needed to memorize facts.… See the full description on the dataset page: https://huggingface.co/datasets/travisp83/agentic-llm-pretraining-1.7b.agentic-backdoor-passive-eval