While maintaining the InternLM2 architecture, various new technical explorations have been conducted, resulting in the next-generation model, InternLM2.5. InternLM2.5 leverages a large amount of synthetic data and continuously uses the InternLM for the iterative process of the model, thanks to the model's capability flywheel. The base model of InternLM2.5 has the following technical characteristics: We have evaluated InternLM2.5 on several important benchmarks using the open-source evaluation tool OpenCompass. Some of the evaluation results are shown in the table below. You are welcome to visit the OpenCompass Leaderboard for more evaluation results. **Limitations:** Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information.
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View details ↗InternLM2.5 has open-sourced a 7 billion parameter base model and a chat model tailored for practical scenarios. The model has the following characteristics: We conducted a comprehensive evaluation of InternLM using the open-source evaluation tool OpenCompass. The evaluation covered five dimensions of capabilities: disciplinary competence, language competence, knowledge competence, inference competence, and comprehension competence. Here are some of the evaluation results, and you can visit the OpenCompass leaderboard for more evaluation results. **Limitations:** Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information.
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View details ↗The second generation of the InternLM model, InternLM2, includes models at two scales: 7B and 20B. For the convenience of users and researchers, we have open-sourced four versions of each scale of the model, which are: - internlm2-base: A high-quality and highly adaptable model base, serving as an excellent starting point for deep domain adaptation. - internlm2 (**recommended**): Built upon the internlm2-base, this version has further pretrained on domain-specific corpus. It shows outstanding performance in evaluations while maintaining robust general language abilities, making it our recommended choice for most applications. - internlm2-chat-sft: Based on the Base model, it undergoes supervised human alignment training. - internlm2-chat (**recommended**): Optimized for conversational interaction on top of the internlm2-chat-sft through RLHF, it excels in instruction adherence, empathetic chatting, and tool invocation. - Effective support for ultra-long contexts of up to 200,000 characters: The model nearly perfectly achieves "finding a needle in a haystack" in long inputs of 200,000 characters. It also leads among open-source models in performance on long-text tasks such as LongBench and L-Eval. - Comprehensive performance enhancement: Compared to the previous generation model, it shows significant improvements in various capabilities, including reasoning, mathematics, and coding.
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View details ↗The second generation of the InternLM model, InternLM2, includes models at two scales: 7B and 20B. For the convenience of users and researchers, we have open-sourced four versions of each scale of the model, which are: - internlm2-base: A high-quality and highly adaptable model base, serving as an excellent starting point for deep domain adaptation. - internlm2 (**recommended**): Built upon the internlm2-base, this version has further pretrained on domain-specific corpus. It shows outstanding performance in evaluations while maintaining robust general language abilities, making it our recommended choice for most applications. - internlm2-chat-sft: Based on the Base model, it undergoes supervised human alignment training. - internlm2-chat (**recommended**): Optimized for conversational interaction on top of the internlm2-chat-sft through RLHF, it excels in instruction adherence, empathetic chatting, and tool invocation. - Effective support for ultra-long contexts of up to 200,000 characters: The model nearly perfectly achieves "finding a needle in a haystack" in long inputs of 200,000 characters. It also leads among open-source models in performance on long-text tasks such as LongBench and L-Eval. - Comprehensive performance enhancement: Compared to the previous generation model, it shows significant improvements in various capabilities, including reasoning, mathematics, and coding.
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View details ↗The second generation of the InternLM model, InternLM2, includes models at two scales: 7B and 20B. For the convenience of users and researchers, we have open-sourced four versions of each scale of the model, which are: - internlm2-base: A high-quality and highly adaptable model base, serving as an excellent starting point for deep domain adaptation. - internlm2 (**recommended**): Built upon the internlm2-base, this version has further pretrained on domain-specific corpus. It shows outstanding performance in evaluations while maintaining robust general language abilities, making it our recommended choice for most applications. - internlm2-chat-sft: Based on the Base model, it undergoes supervised human alignment training. - internlm2-chat (**recommended**): Optimized for conversational interaction on top of the internlm2-chat-sft through RLHF, it excels in instruction adherence, empathetic chatting, and tool invocation. - Effective support for ultra-long contexts of up to 200,000 characters: The model nearly perfectly achieves "finding a needle in a haystack" in long inputs of 200,000 characters. It also leads among open-source models in performance on long-text tasks such as LongBench and L-Eval. - Comprehensive performance enhancement: Compared to the previous generation model, it shows significant improvements in various capabilities, including reasoning, mathematics, and coding.
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View details ↗InternLM2-1.8B is the 1.8 billion parameter version of the second generation InternLM series. In order to facilitate user use and research, InternLM2-1.8B has three versions of open-source models. They are: We have evaluated InternLM2 on several important benchmarks using the open-source evaluation tool OpenCompass. Some of the evaluation results are shown in the table below. You are welcome to visit the OpenCompass Leaderboard for more evaluation results. **Limitations:** Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information.
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View details ↗InternLM2 has open-sourced a 20 billion parameter base model and a chat model tailored for practical scenarios. The model has the following characteristics: - **200K Context window**: Nearly perfect at finding needles in the haystack with 200K-long context, with leading performance on long-context tasks like LongBench and L-Eval. Try it with LMDeploy for 200K-context inference. - **Outstanding comprehensive performance**: Significantly better than the last generation in all dimensions, especially in reasoning, math, code, chat experience, instruction following, and creative writing, with leading performance among open-source models in similar sizes. In some evaluations, InternLM2-Chat-20B may match or even surpass ChatGPT (GPT-3.5).
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View details ↗InternLM2 has open-sourced a 7 billion parameter base model and a chat model tailored for practical scenarios. The model has the following characteristics: - **200K Context window**: Nearly perfect at finding needles in the haystack with 200K-long context, with leading performance on long-context tasks like LongBench and L-Eval. Try it with LMDeploy for 200K-context inference. - **Outstanding comprehensive performance**: Significantly better than the last generation in all dimensions, especially in reasoning, math, code, chat experience, instruction following, and creative writing, with leading performance among open-source models in similar sizes. In some evaluations, InternLM2-Chat-20B may match or even surpass ChatGPT (GPT-3.5).
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View details ↗InternLM2 has open-sourced a 7 billion parameter base model and a chat model tailored for practical scenarios. The model has the following characteristics: InternLM2-Chat-7B-SFT is the SFT version based on InternLM2-Base, and InternLM2-Chat-7B is further trained from InternLM2-Chat-7B-SFT by Online RLHF. We release the SFT version so that the community can study the influence of RLHF deeply. We conducted a comprehensive evaluation of InternLM2 using the open-source evaluation tool OpenCompass. The evaluation covered five dimensions of capabilities: disciplinary competence, language competence, knowledge competence, inference competence, and comprehension competence. Here are some of the evaluation results, and you can visit the OpenCompass leaderboard for more evaluation results.
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