NorMistral-7b-warm is a large Norwegian language model initialized from Mistral-7b-v0.1 and continuously pretrained on a total of 260 billion subword tokens (using six repetitions of open Norwegian texts). This model is a part of the NORA.LLM family developed in collaboration between the Language Technology Group at the University of Oslo, the High Performance Language Technologies (HPLT) project, the National Library of Norway, and the University of Turku. All the models are pre-trained on the same dataset and with the same tokenizer. NorMistral-7b-warm has over 7 billion parameters and is based on the Mistral architecture. The NORA.LLM language model family includes (as of now): - **NorMistral-7b-warm** -- an LLM initialized from Mistral-7b-v0.1 and continuously pretrained on Norwegian data; - **NorMistral-7b-scratch** -- a Mistral-based LLM pretrained from scratch on Norwegian data; - **NorBLOOM-7b-scratch** -- a BLOOM-based LLM pretrained from scratch on Norwegian data.

译文排队中 · 当前显示来源原文

文本生成

输入文本→ 输出文本

价格待核验多模态与渠道费用须分别核对

查看详情 ↗

加入对比来源 / 文档 ↗

normistral-7b-warm-instruct

ltg

This is a model instruction-tuned on open datasets released under the most permissive apache-2.0 licence (in other words, we don't use any datasets generated by ChatGPT) — thus we can release this model under the same license and make it openly available for commercial applications. The model has been finetuned on 4096 context length, twice as many tokens as the base model. The released weights are still a work in progress and they might change in the future. This is the first iteration of instruction-tuning our NorMistral models and it currently uses only the SFT phase without any preference optimization. Please let us know your feedback to improve the model in future releases. 1. We gathered all openly available datasets: Aya, OASST 1, OASST 2, OIG-small-chip2, No Robots, Dolly and Glaive code assistant. 2. These were first manually inspected and filtered, and then automatically filtered with Mixtral-8x7B to remove incorrect, offensive, non-English and American-centric responses. 3. The responses were augmented to be more descriptive by Mixtral-8x7B. 4. Since most of that dataset contains only a single dialogue turn, we generated more turns using Mixtral-8x7B. 5. Finally, we translated the resulting dataset into Bokmål and Nynorsk using NorMistral-7b-warm.

译文排队中 · 当前显示来源原文

文本生成

输入文本→ 输出文本

价格待核验多模态与渠道费用须分别核对

查看详情 ↗

加入对比来源 / 文档 ↗

COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

每月输入 Token 10 M每月输出 Token 2 M1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

MODEL HANDBOOK

读懂模型，再做选择

选型与常见问题

API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

找到适合你的 AI 模型。

探索 AI 模型 10

norbert2

norbert3-base

norbert3-small

norbert3-xs

norbloom-7b-scratch

normistral-11b-long

normistral-11b-thinking-gguf

normistral-11b-warm

normistral-7b-warm

normistral-7b-warm-instruct

暂未找到匹配模型

好模型，也要算好成本。

读懂模型，再做选择

资讯看趋势，工具提效率，模型筑可能。