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Internlm3 8B Instruct
InternLM3-8B-Instruct
I
Internlm3 8B Instruct
AI ModelDevelopment & Tools#Large Language Model#Open Source#Reasoning#Knowledge Processing#Deep LearningStandard PicksOpen Source
Overview :
Developed by the InternLM team, the InternLM3-8B-Instruct is a large language model featuring exceptional reasoning capabilities and proficiency in knowledge-intensive tasks. Despite being trained with only 40 trillion high-quality tokens, it achieves over 75% lower training costs than similar models, while outperforming models such as Llama3.1-8B and Qwen2.5-7B on multiple benchmark tests. It supports deep reasoning modes that tackle complex inference tasks, while also offering smooth user interaction capabilities. The model is open-sourced under the Apache-2.0 license, making it suitable for various applications needing efficient reasoning and knowledge processing.
Target Users :
The target audience includes researchers, developers, and enterprises, suitable for applications requiring efficient reasoning and knowledge processing, such as natural language processing, intelligent assistant development, and complex problem-solving. The open-source nature of InternLM3-8B-Instruct makes it an ideal choice for academic research and commercial applications, helping users improve model performance while reducing costs.
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Use Cases
In natural language processing research, researchers can utilize InternLM3-8B-Instruct for model training and algorithm optimization.
Developers can integrate it into intelligent assistant applications to enhance reasoning and conversational abilities.
Enterprises can employ it for developing knowledge-intensive business systems such as intelligent customer service and data analysis.
Features
Excels in inference and knowledge-intensive tasks, surpassing multiple peer models.
Supports deep reasoning modes for solving intricate inference challenges.
Offers seamless user interaction capabilities with a general response model.
Provides open-source model weights and code for easy use and research by developers.
Comprehensive evaluation using OpenCompass tool covering various capability dimensions.
How to Use
1. Load the model using the Transformers library with the AutoTokenizer and AutoModelForCausalLM classes.
2. Set up system prompts to define the model's role and behavior guidelines.
3. Construct user input messages to interact with the model.
4. Use the model's generate method to produce responses, adjusting parameters for optimized output.
5. Decode the generated responses to obtain the final text results.
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