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探索 AI 模型 271
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查看详情 ↗Llama-4-Scout-17B-16E-Instruct
RedHatAIThe Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. These Llama 4 models mark the beginning of a new era for the Llama ecosystem. We are launching two efficient models in the Llama 4 series, Llama 4 Scout, a 17 billion parameter model with 16 experts, and Llama 4 Maverick, a 17 billion parameter model with 128 experts. **Model Architecture:** The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality.
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查看详情 ↗This model was obtained by quantizing weights of Llama-4-Scout-17B-16E-Instruct to INT4 data type. This optimization reduces the number of bits used to represent weights from 16 to 4, reducing GPU memory requirements by approximately 75%. Weight quantization also reduces disk size requirements by approximately 75%. The llm-compressor library is used for quantization. This model can be deployed efficiently on vLLM, Red Hat Enterprise Linux AI, and Openshift AI, as shown in the example below. The model was evaluated on the OpenLLM leaderboard tasks (v1 and v2), long context RULER, multimodal MMMU, and multimodal ChartQA. All evaluations are obtained through lm-evaluation-harness.
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查看详情 ↗Llama-Guard-4-12B
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查看详情 ↗llama2.c-stories110M-pruned50
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查看详情 ↗Magistral-Small-2506-FP8
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查看详情 ↗Meta-Llama-3.1-405B-Instruct-FP8
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查看详情 ↗Meta-Llama-3.1-70B-FP8
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查看详情 ↗Meta-Llama-3.1-70B-Instruct-FP8
RedHatAI- **Model Architecture:** Meta-Llama-3.1 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Intended Use Cases:** Intended for commercial and research use in multiple languages. Similarly to Meta-Llama-3.1-8B-Instruct, this models is intended for assistant-like chat. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. - **Release Date:** 7/23/2024 - **Version:** 1.0 - **License(s):** llama3.1 - **Model Developers:** Neural Magic Quantized version of Meta-Llama-3.1-70B-Instruct. It achieves an average score of 84.29 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 84.40. This model was obtained by quantizing the weights and activations of Meta-Llama-3.1-70B-Instruct to FP8 data type. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
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查看详情 ↗This model is a quantized version of Meta-Llama-3.1-70B-Instruct. It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model, including multiple-choice, math reasoning, and open-ended text generation. Meta-Llama-3.1-70B-Instruct-quantized.w4a16 achieves 100.0% recovery for the Arena-Hard evaluation, 99.4% for OpenLLM v1 (using Meta's prompting when available), 97.4% for OpenLLM v2, 101.0% for HumanEval pass@1, and 99.2% for HumanEval+ pass@1. This model was obtained by quantizing the weights of Meta-Llama-3.1-70B-Instruct to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. Only the weights of the linear operators within transformers blocks are quantized. Symmetric per-group quantization is applied, in which a linear scaling per group of 128 parameters maps the INT4 and floating point representations of the quantized weights. The GPTQ algorithm is applied for quantization, as implemented in the AutoGPTQ library. GPTQ used a 1% damping factor and 512 sequences of 8,192 random tokens.
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查看详情 ↗Meta-Llama-3.1-8B-FP8
RedHatAIQuantized version of Meta-Llama-3.1-8B. It achieves an average score of 65.90 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 66.47. This model was obtained by quantizing the weights and activations of Meta-Llama-3.1-8B to FP8 data type, ready for inference with vLLM built from source. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a single linear scaling maps the FP8 representations of the quantized weights and activations. LLM Compressor is used for quantization with 512 sequences of UltraChat.
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查看详情 ↗Meta-Llama-3.1-8B-Instruct-FP8
RedHatAI- **Model Architecture:** Meta-Llama-3.1 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Intended Use Cases:** Intended for commercial and research use in multiple languages. Similarly to Meta-Llama-3.1-8B-Instruct, this models is intended for assistant-like chat. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. - **Release Date:** 7/23/2024 - **Version:** 1.0 - **License(s):** llama3.1 - **Model Developers:** Neural Magic Quantized version of Meta-Llama-3.1-8B-Instruct. It achieves an average score of 73.44 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 73.79. This model was obtained by quantizing the weights and activations of Meta-Llama-3.1-8B-Instruct to FP8 data type, ready for inference with vLLM built from source. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
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查看详情 ↗This model is a quantized version of Meta-Llama-3.1-8B-Instruct. It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model, including multiple-choice, math reasoning, and open-ended text generation. Meta-Llama-3.1-8B-Instruct-FP8-dynamic achieves 105.4% recovery for the Arena-Hard evaluation, 99.7% for OpenLLM v1 (using Meta's prompting when available), 101.2% for OpenLLM v2, 100.0% for HumanEval pass@1, and 101.0% for HumanEval+ pass@1. This model was obtained by quantizing the weights and activations of Meta-Llama-3.1-8B-Instruct to FP8 data type, ready for inference with vLLM built from source. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-channel quantization is applied, in which a linear scaling per output dimension maps the FP8 representations of the quantized weights and activations. Activations are also quantized on a per-token dynamic basis. LLM Compressor is used for quantization.
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查看详情 ↗- **Model Architecture:** LlamaForCausalLM - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** INT4 - **Intended Use Cases:** Intended for commercial and research use in English. Similarly to Meta-Llama-3.1-8B-Instruct, this models is intended for assistant-like chat. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. - **Release Date:** 7/26/2024 - **Version:** 1.0 - **Validated on:** RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5 - **License(s):** Llama3.1 - **Model Developers:** Neural Magic This model is a quantized version of Meta-Llama-3.1-8B-Instruct. It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model, including multiple-choice, math reasoning, and open-ended text generation. Meta-Llama-3.1-8B-Instruct-quantized.w4a16 achieves 93.0% recovery for the Arena-Hard evaluation, 98.9% for OpenLLM v1 (using Meta's prompting when available), 96.1% for OpenLLM v2, 99.7% for HumanEval pass@1, and 97.4% for HumanEval+ pass@1. This model was obtained by quantizing the weights of Meta-Llama-3.1-8B-Instruct to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.
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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。