InternLM3-8B-Instruct
8B
Model parameter quantity
Shanghai AI Laboratory
Affiliated organization
Open Source
License Type
January 15, 2025
Release time
Model Introduction
InternLM3-8B-Instruct is a high-performance, low-cost 8-billion parameter instruction model designed for complex reasoning and smooth interaction, supporting advanced reasoning tasks and knowledge-intensive applications through deep thinking and normal response modes.
Comprehensive score
Language dialogue
Knowledge reserve
Reasoning association
Mathematical calculation
Code writing
Command following
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Language comprehension ability
Often makes semantic misjudgments, leading to obvious logical disconnects in responses.
6.1
Knowledge coverage scope
Possesses core knowledge of mainstream disciplines, but has limited coverage of cutting-edge interdisciplinary fields.
7.7
Reasoning ability
Unable to maintain coherent reasoning chains, often causing inverted causality or miscalculations.
5.1
Model comparison
InternLM3-8B-Instruct vs Qwen2.5-7B-Instruct
Like Qwen2, the Qwen2.5 language models support up to 128K tokens and can generate up to 8K tokens. They also maintain multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
InternLM3-8B-Instruct vs GPT-4o-mini-20240718
GPT-4o-mini is an API model produced by OpenAI, with the specific version number being gpt-4o-mini-2024-07-18.
InternLM3-8B-Instruct vs GPT-4o-mini-20240718
GPT-4o-mini is an API model produced by OpenAI, with the specific version number being gpt-4o-mini-2024-07-18.
InternLM3-8B-Instruct vs Gemini-2.5-Pro-Preview-05-06
Gemini 2.5 Pro is a model released by Google DeepMind artificial intelligence research team, using version number Gemini-2.5-Pro-Preview-05-06.
InternLM3-8B-Instruct vs DeepSeek-V2-Chat-0628
DeepSeek-V2 is a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token. Compared with DeepSeek 67B, DeepSeek-V2 achieves stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times.
Related model
InternLM2-Chat-20B
The second generation of the InternLM model, InternLM2, includes models at two scales: 7B and 20B.
InternLM2.5-Chat-7B
The 2.5 generation of the InternLM model, InternLM2.5, includes models at three scales: 1.8B, 7B and 20B.
InternLM2-Chat-20B
The second generation of the InternLM model, InternLM2, includes models at two scales: 7B and 20B.
InternLM2.5-Chat-7B
The 2.5 generation of the InternLM model, InternLM2.5, includes models at three scales: 1.8B, 7B and 20B.
InternLM2.5-Chat-7B
The 2.5 generation of the InternLM model, InternLM2.5, includes models at three scales: 1.8B, 7B and 20B.
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