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Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 28, 2026 · Artificial Intelligence

Open‑Source 35B Intern‑S2‑Preview Rivals Trillion‑Parameter Models on Scientific Benchmarks

The open‑source 35‑billion‑parameter Intern‑S2‑Preview model achieves scientific‑task performance comparable to trillion‑parameter models, thanks to full‑link “general‑specialized” training, reinforced‑learning scaling, and hardware‑aware optimizations, and it outperforms leading closed‑source models on benchmarks such as MolecularIQ and crystal‑structure generation.

InternLMLarge Language ModelOpen Source
0 likes · 11 min read
Open‑Source 35B Intern‑S2‑Preview Rivals Trillion‑Parameter Models on Scientific Benchmarks
AIWalker
AIWalker
Jan 18, 2025 · Artificial Intelligence

How InternLM 3.0 Achieves High Performance with Just 4 TB of Training Data

Shanghai AI Laboratory’s InternLM 3.0 upgrade demonstrates that a refined 4 TB token dataset can boost a large‑language model’s performance beyond that of open‑source peers trained on 18 TB, cutting training cost by over 75% while merging regular dialogue with deep reasoning capabilities.

AI evaluationInternLMLarge Language Model
0 likes · 9 min read
How InternLM 3.0 Achieves High Performance with Just 4 TB of Training Data
AIWalker
AIWalker
Jan 17, 2025 · Artificial Intelligence

InternLM 3.0: Boosting Model Performance with Only 4 TB of Training Data

Shanghai AI Laboratory’s InternLM 3.0 upgrade demonstrates that refining data quality—measured as intelligence‑per‑token—can replace massive datasets, achieving higher reasoning and dialogue capabilities with just 4 TB of tokens, cutting training cost by over 75 % while approaching GPT‑4‑level performance.

AI researchInternLMLarge Language Model
0 likes · 9 min read
InternLM 3.0: Boosting Model Performance with Only 4 TB of Training Data
AIWalker
AIWalker
Jan 16, 2025 · Artificial Intelligence

How InternLM 3.0 Achieves High Performance with Just 4 TB of Training Data

InternLM 3.0 (InternLM‑3) upgrades the Shusheng‑PuYu model by refining data to boost "thinking density", using only 4 TB of tokens to surpass peer open‑source models, cutting training cost by over 75% while merging ordinary dialogue with deep reasoning capabilities.

InternLMLarge Language Modeldata efficiency
0 likes · 9 min read
How InternLM 3.0 Achieves High Performance with Just 4 TB of Training Data
OPPO Kernel Craftsman
OPPO Kernel Craftsman
Mar 29, 2024 · Artificial Intelligence

InternLM Model Research and XTuner Practical Guide (Part 1): DataLoader, Model Conversion, Merging, and Inference

The guide walks through fine‑tuning InternLM‑Chat‑7B with XTuner, showing how to build a DataLoader from a HuggingFace Dataset, convert a LoRA .pth checkpoint to HuggingFace format, merge the adapter into the base model, run inference, and adapt the process for custom datasets and 4‑bit quantization experiments.

DataLoaderFineTuningInternLM
0 likes · 27 min read
InternLM Model Research and XTuner Practical Guide (Part 1): DataLoader, Model Conversion, Merging, and Inference
OPPO Kernel Craftsman
OPPO Kernel Craftsman
Mar 22, 2024 · Artificial Intelligence

InternLM Model Fine-Tuning Tutorial with XTuner: Chat Format and Practical Implementation Guide

This tutorial walks through fine‑tuning Shanghai AI Lab’s open‑source InternLM models with XTuner, explaining chat‑format conventions, loading and inference (including multimodal InternLM‑XComposer), dataset preparation, configuration sections, DeepSpeed acceleration, and memory‑efficient QLoRA details for 7‑B‑parameter chat models.

Chat FormatDeepSpeedInternLM
0 likes · 22 min read
InternLM Model Fine-Tuning Tutorial with XTuner: Chat Format and Practical Implementation Guide