internlm

About

What internlm is

InternLM [](https://github.com/internLM/OpenCompass/) 💻Github Repo • 🤔Reporting Issues Introduction InternLM has open-sourced a 7 billion parameter base model and a chat model tailored for practical scenarios. The model has the following characteristics: - It leverages trillions of high-quality tokens for training to establish a powerful knowledge base. - It supports an 8k context window length, enabling longer input sequences and stronger reasoning capabilities. - It provides a versatile toolset for users to flexibly build their own workflows. InternLM-7B Performance Evaluation 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. - The evaluation results were obtained from OpenCompass 20230706 (some data marked with , which means come from the original papers), and evaluation configuration can be found in the configuration files provided by OpenCompass. - The evaluation data may have numerical differences due to the version iteration of OpenCompass, so please refer to the latest evaluation results of OpenCompass. 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. Import from Transformers To load the InternLM 7B Chat model using Transformers, use the following code: The responses can be streamed using streamchat: Open Source License The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow free

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys internlm for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (internlm below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"internlm","messages":[{"role":"user","content":"Hello"}]}'

Details

Languages, data & research

Licence

Commercial licence needed

Sources

Weights & code

Explore

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