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价格待核验多模态与渠道费用须分别核对
查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 36
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查看详情 ↗This Dolphin is *really good* at coding, I trained with a lot of coding data. It is *very* obedient but it is not DPO tuned - so you still might need to encourage it in the system prompt as I show in the below examples. New in 2.6 - Fixed a training configuration issue that improved the quality a lot - Due to popular demand, added back samantha-based empathy data - Replaced synthia and pure-dove with Capybara This model is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant to any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
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查看详情 ↗My appreciation for the sponsors of Dolphin 2.8: - Crusoe Cloud - provided excellent on-demand 10xL40S node - Winston Sou - Along with a generous anonymous sponsor, donated a massive personally owned compute resource! - Abacus AI - my employer and partner in many things. This model is based on Mistral-7b-v0.2 a new base model released by MistralAI on March 23, 2024 but they have not yet published on HuggingFace. Thanks to @alpindale for converting / publishing. Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant to any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
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查看详情 ↗Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations We have retrained our LLama-3-70b fine tune to address behavioral issues in the initial 2.9 dataset. Specifically, Systemchat was causing the model to be *too* reliant on the system prompt. Additionally, it had an occasional quirk that would cause the model to overly reference the system prompt. We also found generation length was at times not sufficient for any given task. We identified the culprit as Ultrachat. Accounting for these concerns, we removed systemchat and ultrachat from the dataset. It is otherwise identical to dolphin-2.9. Our appreciation for the sponsors of Dolphin 2.9.1: - Crusoe Cloud - provided excellent on-demand 8xL40S node - OnDemand - provided inference sponsorship
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查看详情 ↗Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations We have retrained our LLama-3-8b fine tune to address behavioral issues in the initial 2.9 dataset. Specifically, Systemchat was causing the model to be *too* reliant on the system prompt. Additionally, it had an occasional quirk that would cause the model to overly reference the system prompt. We also found generation length was at times not sufficient for any given task. We identified the culprit as Ultrachat. Accounting for these concerns, we removed systemchat and ultrachat from the dataset. It is otherwise identical to dolphin-2.9. Our appreciation for the sponsors of Dolphin 2.9.1: - Crusoe Cloud - provided excellent on-demand 8xL40S node
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查看详情 ↗Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations The model is an extracted expert using SLERP and a custom script that we've open-sourced. It extracts a single expert which is the combined SLERP of all 8 experts from a Mixtral architecture. We decided to not fully convert to a dense model, for the sake of trying to keep as much of the original model's performance as possible, as this process is already quite surgical and there are a lot of variables to take into account. Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
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查看详情 ↗Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations This is our most spectacular outcome ever. FFT, all parameters, 16bit. 77.4 MMLU on 34b. And it talks like a dream. Although the max positional embeddings is 4k, we used rope theta of 1000000.0 and we trained with sequence length 8k. We plan to train on the upcoming 32k version as well.
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查看详情 ↗Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations This is our most spectacular outcome ever. FFT, all parameters, 16bit. 70.9 MMLU on 9b! And it talks like a dream. Although the max positional embeddings is 4k, we used rope theta of 1000000.0 and we trained with sequence length 12k. We plan to train on the upcoming 32k version as well.
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查看详情 ↗Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations New in 2.9.2 is SystemChat 2.0 - a dataset designed to teach Dolphin to obey the system prompt, even over a long conversation. My appreciation for the sponsors of Dolphin 2.9.2: - Crusoe Cloud - provided excellent on-demand 8xH100 node - OnDemand - provided inference sponsorship, enabling creation of SystemChat
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查看详情 ↗Curated and trained by Eric Hartford, Lucas Atkins, Fernando Fernandes, and with help from the community of Cognitive Computations Our appreciation for the sponsor of Dolphin 2.9.2: - Crusoe Cloud - provided excellent on-demand 8xL40Snode This model is based on Phi-3-Medium-Instruct-4k, and is governed by the MIT license with which Microsoft released Phi-3.
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查看详情 ↗Curated and trained by Eric Hartford, Lucas Atkins, Fernando Fernandes, and with help from the community of Cognitive Computations Uncensored by FailSpy Our appreciation for the sponsor of Dolphin 2.9.2: - Crusoe Cloud - provided excellent on-demand 8xL40Snode This model is based on Phi-3-Medium-Instruct-4k, and is governed by the MIT license with which Microsoft released Phi-3.
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查看详情 ↗Our appreciation for the sponsors of Dolphin 2.9.3: - Crusoe Cloud - provided excellent on-demand 8xL40S node Dolphin-2.9.3 has a variety of instruction following, conversational, and coding skills. It also has initial agentic abilities and supports function calling. Dolphin is uncensored. We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations Our appreciation for the sponsors of Dolphin 2.9.3: - Crusoe Cloud - provided excellent on-demand 8xH100 node - OnDemand - provided inference sponsorship Dolphin-2.9.3 has a variety of instruction following, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
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查看详情 ↗Our appreciation for the sponsors of Dolphin 2.9.4: - Crusoe Cloud - provided excellent on-demand 8xL40S node Dolphin-2.9.4 has a variety of instruction following, conversational, and coding skills. It also has agentic abilities and supports function calling. It is especially trained to obey the system prompt, and follow instructions in many languages. Dolphin is uncensored. We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
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
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
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。