Model reference · open weights
internlm2_5 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 InternLM2.5 has open-sourced a 7 billion parameter base model and a chat model tailored for practical scenarios. The model has the following characteristics: - Outstanding reasoning capability: State-of-the-art performance on Math reasoning, surpassing models like Llama3 and Gemma2-9B. - 1M Context window: Nearly perfect at finding needles in the haystack with 1M-long context, with leading performance on long-context tasks like LongBench. Try it with LMDeploy for 1M-context inference. - Stronger tool use: InternLM2.5 supports gathering information from more than 100 web pages, corresponding implementation has be released in MindSearch. InternLM2.5 has better tool utilization-related capabilities in instruction following, tool selection and reflection. See examples. InternLM2.5-7B-Chat 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 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 probabilist
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 | 1 |
| Runs with | transformers |
| Released | 2024-06-27 |
| Popularity | 39k downloads / month |
| Likes | 201 |
| 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.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| internlm2_5-7b-chat | 7.7B | BF16 | ~17.8 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys internlm2-5 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (internlm2-5 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":"internlm2-5","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 ↗