AutoCompressor-Llama-2-7b-6k
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 17
gemma-2-9b-it-SimPO
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查看详情 ↗Llama-3-8B-ProLong-512k-Base
princeton-nlp**ProLong** ( Pr incet o n long -context language models) is a family of long-context models that are continued trained and supervised fine-tuned from Llama-3-8B, with a maximum context window of 512K tokens. Our main ProLong model is one of the best-performing long-context models at the 10B scale (evaluated by HELMET). To train this strong long-context model, we conduct thorough ablations on the long-context pre-training data, SFT data, and numerous other design choices. We demonstrate our findings in our paper, How to Train Long-Context Language Models (Effectively). Here are some quick facts about our main ProLong model: princeton-nlp/Llama-3-8B-ProLong-512k-Instruct. * Base model: meta-llama/Meta-Llama-3-8B-Instruct * Long-context continued training: 20B tokens on 64K training data (princeton-nlp/prolong-data-64K), and 20B tokens on 512K training data (princeton-nlp/prolong-data-512K) * Supervised fine-tuning (SFT): UltraChat * Maximum context window: 512K tokens
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查看详情 ↗Llama-3-8B-ProLong-512k-Instruct
princeton-nlp**ProLong** ( Pr incet o n long -context language models) is a family of long-context models that are continued trained and supervised fine-tuned from Llama-3-8B, with a maximum context window of 512K tokens. Our main ProLong model is one of the best-performing long-context models at the 10B scale (evaluated by HELMET). To train this strong long-context model, we conduct thorough ablations on the long-context pre-training data, SFT data, and numerous other design choices. We demonstrate our findings in our paper, How to Train Long-Context Language Models (Effectively). Here are some quick facts about our main ProLong model: princeton-nlp/Llama-3-8B-ProLong-512k-Instruct. * Base model: meta-llama/Meta-Llama-3-8B-Instruct * Long-context continued training: 20B tokens on 64K training data (princeton-nlp/prolong-data-64K), and 20B tokens on 512K training data (princeton-nlp/prolong-data-512K) * Supervised fine-tuning (SFT): UltraChat * Maximum context window: 512K tokens
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查看详情 ↗Llama-3-8B-ProLong-64k-Base
princeton-nlp**ProLong** ( Pr incet o n long -context language models) is a family of long-context models that are continued trained and supervised fine-tuned from Llama-3-8B, with a maximum context window of 512K tokens. Our main ProLong model is one of the best-performing long-context models at the 10B scale (evaluated by HELMET). To train this strong long-context model, we conduct thorough ablations on the long-context pre-training data, SFT data, and numerous other design choices. We demonstrate our findings in our paper, How to Train Long-Context Language Models (Effectively). Here are some quick facts about our main ProLong model: princeton-nlp/Llama-3-8B-ProLong-512k-Instruct. * Base model: meta-llama/Meta-Llama-3-8B-Instruct * Long-context continued training: 20B tokens on 64K training data (princeton-nlp/prolong-data-64K), and 20B tokens on 512K training data (princeton-nlp/prolong-data-512K) * Supervised fine-tuning (SFT): UltraChat * Maximum context window: 512K tokens
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查看详情 ↗Llama-3-8B-ProLong-64k-Instruct
princeton-nlp**ProLong** ( Pr incet o n long -context language models) is a family of long-context models that are continued trained and supervised fine-tuned from Llama-3-8B, with a maximum context window of 512K tokens. Our main ProLong model is one of the best-performing long-context models at the 10B scale (evaluated by HELMET). To train this strong long-context model, we conduct thorough ablations on the long-context pre-training data, SFT data, and numerous other design choices. We demonstrate our findings in our paper, How to Train Long-Context Language Models (Effectively). Here are some quick facts about our main ProLong model: princeton-nlp/Llama-3-8B-ProLong-512k-Instruct. * Base model: meta-llama/Meta-Llama-3-8B-Instruct * Long-context continued training: 20B tokens on 64K training data (princeton-nlp/prolong-data-64K), and 20B tokens on 512K training data (princeton-nlp/prolong-data-512K) * Supervised fine-tuning (SFT): UltraChat * Maximum context window: 512K tokens
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查看详情 ↗queen_pawn-8
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查看详情 ↗QuRater-1.3B
princeton-nlpThe model is a sequence classification model that predicts quality ratings across 4 critera as outputs. The quality ratings (which are unnormalized) can be accessed via the 4 logits in the output of the model. They are in the following order: * Logit 0: Writing Style * Logit 1: Required Expertise * Logit 2: Facts and Trivia * Logit 3: Educational Value **Note:** The model was only fine-tuned on sequences of up to 512 tokens, and should not be used for longer documents. Instead, compute the quality ratings for windows of up to 512 token and average them weighted by the window length. In the paper, we document various types of bias that are present in the quality ratings from the QuRater model (biases related to domains, topics, social roles, regions and languages - see Section 6 of the paper). Hence, be aware that data selection with QuRating could have unintended and harmful effects on the language model that is being trained. We strongly recommend a comprehensive evaluation of the language model for these and other types of bias, particularly before real-world deployment. We hope that releasing the data/models can facilitate future research aimed at uncovering and mitigating such biases. Note that the quality ratings do not measure the social or literary value of a text and should *not* be used for textual or demographic studies.
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查看详情 ↗Sheared-LLaMA-1.3B
princeton-nlp**Paper**: https://arxiv.org/pdf/2310.06694.pdf **Code**: https://github.com/princeton-nlp/LLM-Shearing **Models**: Sheared-LLaMA-1.3B, Sheared-LLaMA-2.7B **Pruned Models without Continued Pre-training**: Sheared-LLaMA-1.3B-Pruned, Sheared-LLaMA-2.7B-Pruned **Instruction-tuned Models**: Sheared-LLaMA-1.3B-ShareGPT, Sheared-LLaMA-2.7B-ShareGPT Sheared-LLaMA-1.3B is a model pruned and further pre-trained from meta-llama/Llama-2-7b-hf. We dynamically load data from different domains in the RedPajama dataset to prune and contune pre-train the model. We use 0.4B tokens for pruning and 50B tokens for continued pre-training the pruned model. This model can be loaded with HuggingFace via We evaluate on an extensive set of downstream tasks including reasoning, reading comprehension, language modeling and knowledge intensive tasks. Our Sheared-LLaMA models outperform existing large language models.
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查看详情 ↗Sheared-LLaMA-1.3B-ShareGPT
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查看详情 ↗Sheared-LLaMA-2.7B
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查看详情 ↗sup-simcse-bert-base-uncased
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查看详情 ↗sup-simcse-roberta-base
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查看详情 ↗sup-simcse-roberta-large
princeton-nlpSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. The model craters note in the Github Repository > We train unsupervised SimCSE on 106 randomly sampled sentences from English Wikipedia, and train supervised SimCSE on the combination of MNLI and SNLI datasets (314k).
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查看详情 ↗unsup-simcse-bert-base-uncased
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查看详情 ↗unsup-simcse-roberta-large
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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。