Model reference · open weights
internlm2 is an open-weight language model from internlm, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
InternLM [](https://github.com/internLM/OpenCompass/) 💻Github Repo • 🤔Reporting Issues • 📜Technical Report 👋 join us on <a href="https://discord.gg/xa29JuW87d" target="blank"Discord</a and <a href="https://github.com/InternLM/InternLM/assets/25839884/a6aad896-7232-4220-ac84-9e070c2633ce" target="blank"WeChat</a Introduction 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). - Code interpreter & Data analysis: With code interpreter, InternLM2-Chat-20B obtains compatible performance with GPT-4 on GSM8K and MATH. InternLM2-Chat also provides data analysis capability. - Stronger tool use: Based on better tool utilization-related capabilities in instruction following, tool selection and reflection, InternLM2 can support more kinds of agents and multi-step tool calling for complex tasks. See examples. InternLM2-Chat-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 (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 du
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | internlm |
|---|---|
| Type | Language models |
| Parameters (lead) | 7.7B |
| Context | 32k tokens |
| Variants | 3 |
| Runs with | transformers |
| Released | 2024-01-10 |
| Popularity | 73k downloads / month |
| Likes | 88 |
| Licence | Commercial licence needed |
How it works
Variants
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
Once AxForge deploys internlm2 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (internlm2 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":"internlm2","messages":[{"role":"user","content":"Hello"}]}'
Licence
The weights are open but its licence needs a commercial agreement for business use. AxForge can arrange that licence and host the model for you — you pay AxForge, we settle with the model’s maker. Ask us for a quote. Read the licence ↗