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Collections including paper arxiv:2401.01335

Synthetic Data Generation

about 4 hours ago

Textbooks Are All You Need

Paper • 2306.11644 • Published Jun 20, 2023 • 142
Textbooks Are All You Need II: phi-1.5 technical report

Paper • 2309.05463 • Published Sep 11, 2023 • 87
TinyStories: How Small Can Language Models Be and Still Speak Coherent English?

Paper • 2305.07759 • Published May 12, 2023 • 33
Scaling Synthetic Data Creation with 1,000,000,000 Personas

Paper • 2406.20094 • Published Jun 28 • 94

Papers - Fine-tuning

Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning

Paper • 2310.20587 • Published Oct 31, 2023 • 16
SELF: Language-Driven Self-Evolution for Large Language Model

Paper • 2310.00533 • Published Oct 1, 2023 • 2
QLoRA: Efficient Finetuning of Quantized LLMs

Paper • 2305.14314 • Published May 23, 2023 • 45
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

Paper • 2309.14717 • Published Sep 26, 2023 • 44

Self Improvement

Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Paper • 2401.01335 • Published Jan 2 • 64

synthetic_datasets

Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Paper • 2401.01335 • Published Jan 2 • 64

DIBT Prompt collective SPIN

This collection contains resources related to the replication of SPIN with the dibt prompt collective dataset

argilla/zephyr-7b-spin-iter0-v0

Text Generation • Updated Mar 13 • 14 • 1
argilla/zephyr-7b-spin-iter1-v0

Text Generation • Updated Mar 13 • 14 • 1
argilla/zephyr-7b-spin-iter2-v0

Text Generation • Updated Mar 13 • 19 • 1
argilla/zephyr-7b-spin-iter3-v0

Text Generation • Updated Mar 13 • 7 • 8

ibm/AttaQ

Viewer • Updated Jan 26 • 1.4k • 961 • 11
snorkelai/snorkel-curated-instruction-tuning

Preview • Updated Mar 11 • 120 • 8
corbyrosset/researchy_questions

Viewer • Updated Feb 29 • 96.4k • 1.45k • 24
argilla/ultrafeedback-binarized-preferences

Viewer • Updated Nov 30, 2023 • 63.6k • 280 • 66

Chain-of-Thought Reasoning Without Prompting

Paper • 2402.10200 • Published Feb 15 • 99
How to Train Data-Efficient LLMs

Paper • 2402.09668 • Published Feb 15 • 38
BitDelta: Your Fine-Tune May Only Be Worth One Bit

Paper • 2402.10193 • Published Feb 15 • 17
A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts

Paper • 2402.09727 • Published Feb 15 • 35

Learning from feedback dir

Suppressing Pink Elephants with Direct Principle Feedback

Paper • 2402.07896 • Published Feb 12 • 9
Policy Improvement using Language Feedback Models

Paper • 2402.07876 • Published Feb 12 • 5
Direct Language Model Alignment from Online AI Feedback

Paper • 2402.04792 • Published Feb 7 • 29
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models

Paper • 2401.01335 • Published Jan 2 • 64

Metadata Might Make Language Models Better

Paper • 2211.10086 • Published Nov 18, 2022 • 4
Empirical Analysis of the Strengths and Weaknesses of PEFT Techniques for LLMs

Paper • 2304.14999 • Published Apr 28, 2023 • 2
PEFT for Speech: Unveiling Optimal Placement, Merging Strategies, and Ensemble Techniques

Paper • 2401.02122 • Published Jan 4 • 2
Zephyr: Direct Distillation of LM Alignment

Paper • 2310.16944 • Published Oct 25, 2023 • 121

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29 • 52
Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 18
ChunkAttention: Efficient Self-Attention with Prefix-Aware KV Cache and Two-Phase Partition

Paper • 2402.15220 • Published Feb 23 • 19
Linear Transformers are Versatile In-Context Learners

Paper • 2402.14180 • Published Feb 21 • 6

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