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探索 AI 模型 32

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Bielik-1.5B-v3

speakleash

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

基础资料已收录 · 缺失资料后续补充

文本生成
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价格待核验多模态与渠道费用须分别核对
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Bielik-11B-v2.3-Instruct is a generative text model featuring 11 billion parameters. It is a linear merge of the Bielik-11B-v2.0-Instruct, Bielik-11B-v2.1-Instruct, and Bielik-11B-v2.2-Instruct models, which are instruct fine-tuned versions of the Bielik-11B-v2. Forementioned model stands as a testament to the unique collaboration between the open-science/open-souce project SpeakLeash and the High Performance Computing (HPC) center: ACK Cyfronet AGH. Developed and trained on Polish text corpora, which has been cherry-picked and processed by the SpeakLeash team, this endeavor leverages Polish large-scale computing infrastructure, specifically within the PLGrid environment, and more precisely, the HPC centers: ACK Cyfronet AGH. The creation and training of the Bielik-11B-v2.3-Instruct was propelled by the support of computational grant number PLG/2024/016951, conducted on the Athena and Helios supercomputer, enabling the use of cutting-edge technology and computational resources essential for large-scale machine learning processes. As a result, the model exhibits an exceptional ability to understand and process the Polish language, providing accurate responses and performing a variety of linguistic tasks with high precision. The SpeakLeash team is working on their own set of instructions in Polish, which is continuously being expanded and refined by annotators. A portion of these instructions, which had been manually verified and corrected, has been utilized for training purposes. Moreover, due to the limited availability of high-quality instructions in Polish, synthetic instructions were generated with Mixtral 8x22B and used in training. The dataset used for training comprised over 20 million instructions, consisting of more than 10 billion tokens. The instructions varied in quality, leading to a deterioration in the model’s performance. To counteract this while still allowing ourselves to utilize the aforementioned datasets, several improvements were introduced: * Weighted tokens level loss - a strategy inspired by offline reinforcement learning and C-RLFT * Adaptive learning rate inspired by the study on Learning Rates as a Function of Batch Size * Masked promp

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DISCLAIMER: Be aware that quantised models show reduced response quality and possible hallucinations! If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our Discord SpeakLeash.

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文本生成
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DISCLAIMER: Be aware that quantised models show reduced response quality and possible hallucinations! The GGUF file can be used with Ollama. To do this, you need to import the model using the configuration defined in the Modfile. For model eg. Bielik-11B-v3.0-Instruct.Q4_K_M.gguf (full path to model location) Modfile looks like: Here is an incomplete list of clients and libraries that are known to support GGUF: * llama.cpp. The source project for GGUF. Offers a CLI and a server option. * text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * LM Studio, an easy-to-use and powerful local GUI for Windows, macOS (Silicon) and Linux, with GPU acceleration * LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection. * Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use. * ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note ctransformers has not been updated in a long time and does not support many recent models.

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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
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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。

1 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。

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