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探索 AI 模型 271
This model is a quantized version of Meta-Llama-3.1-8B-Instruct. It was evaluated on a several tasks to assess 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.w8a8 achieves 105.4% recovery for the Arena-Hard evaluation, 100.3% for OpenLLM v1 (using Meta's prompting when available), 101.5% for OpenLLM v2, 99.7% for HumanEval pass@1, and 98.8% for HumanEval+ pass@1. This model was obtained by quantizing the weights of Meta-Llama-3.1-8B-Instruct to INT8 data type. This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x). Weight quantization also reduces disk size requirements by approximately 50%. Only weights and activations of the linear operators within transformers blocks are quantized. Weights are quantized with a symmetric static per-channel scheme, where a fixed linear scaling factor is applied between INT8 and floating point representations for each output channel dimension. Activations are quantized with a symmetric dynamic per-token scheme, computing a linear scaling factor at runtime for each token between INT8 and floating point representations. The GPTQ algorithm is applied for quantization, as implemented in the llm-compressor library. GPTQ used a 1% damping factor and 256 sequences of 8,192 random tokens.
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查看详情 ↗Meta-Llama-3.1-8B-quantized.w8a8
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查看详情 ↗Meta-Llama-3-70B-Instruct-FP8
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查看详情 ↗Meta-Llama-3-8B-Instruct-FP8
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查看详情 ↗Meta-Llama-3-8B-Instruct-FP8-KV
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查看详情 ↗Ministral-3-14B-Instruct-2512
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查看详情 ↗This model was obtained by quantizing the weights and activations of mistralai/Ministral-3-14B-Instruct-2512-BF16 to FP8 data type, ready for inference with vLLM. 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. The model was evaluated on the ifeval and mmmu using lm-evaluation-harness, on reasoning tasks using lighteval. vLLM was used for all evaluations.
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查看详情 ↗Ministral-3-3B-Instruct-2512
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查看详情 ↗Mistral-7B-Instruct-v0.3-FP8
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查看详情 ↗Mistral-Medium-3.5-128B
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查看详情 ↗Mistral-Nemo-Instruct-2407-FP8
RedHatAIQuantized version of Mistral-Nemo-Instruct-2407. It achieves an average score of 71.28 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 71.61. This model was obtained by quantizing the weights and activations of Mistral-Nemo-Instruct-2407 to FP8 data type, ready for inference with vLLM >= 0.5.0. 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. AutoFP8 is used for quantization with 512 sequences of UltraChat.
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查看详情 ↗- **Model Architecture:** Mistral-Small-24B-Instruct-2501 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Release Date:** 3/1/2025 - **Version:** 1.0 - **Validated on:** RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5 - **Model Developers:** Neural Magic Quantized version of Mistral-Small-24B-Instruct-2501. It achieves an average score of 78.88 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 79.45. This model was obtained by quantizing the weights and activations to FP8 data type, ready for inference with vLLM. 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.
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查看详情 ↗This model was obtained by quantizing activations and weights of Mistral-Small-24B-Instruct-2501 to INT8 data type. This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x). Weight quantization also reduces disk size requirements by approximately 50%. Only weights and activations of the linear operators within transformers blocks are quantized. Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme. A combination of the SmoothQuant and GPTQ algorithms is applied for quantization, as implemented in the llm-compressor library.
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查看详情 ↗- **Model Architecture:** Mistral3ForConditionalGeneration - **Input:** Text / Image - **Output:** Text - **Model Optimizations:** - **Activation quantization:** FP8 - **Weight quantization:** FP8 - **Intended Use Cases:** It is ideal for: - Fast-response conversational agents. - Low-latency function calling. - Subject matter experts via fine-tuning. - Local inference for hobbyists and organizations handling sensitive data. - Programming and math reasoning. - Long document understanding. - Visual understanding. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages not officially supported by the model. - **Release Date:** 04/15/2025 - **Version:** 1.0 - **Validated on:** RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5 - **Model Developers:** RedHat (Neural Magic) This model was obtained by quantizing activations and weights of Mistral-Small-3.1-24B-Instruct-2503 to FP8 data type. This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x). Weight quantization also reduces disk size requirements by approximately 50%. Only weights and activations of the linear operators within transformers blocks are quantized. Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme. The llm-compressor library is used for quantization.
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查看详情 ↗This model was obtained by quantizing the weights of Mistral-Small-3.1-24B-Instruct-2503 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. Weights are quantized using a symmetric per-group scheme, with group size 128. The GPTQ algorithm is applied for quantization, as implemented in the llm-compressor library. The model was evaluated on the OpenLLM leaderboard tasks (version 1), MMLU-pro, GPQA, HumanEval and MBPP. Non-coding tasks were evaluated with lm-evaluation-harness, whereas coding tasks were evaluated with a fork of evalplus. vLLM is used as the engine in all cases.
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查看详情 ↗- **Model Architecture:** unsloth/Mistral-Small-3.2-24B-Instruct-2506 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP4 - **Activation quantization:** FP4 - **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:** 10/29/2025 - **Version:** 1.0 - **Model Developers:** RedHatAI This model is a quantized version of unsloth/Mistral-Small-3.2-24B-Instruct-2506. It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model. This model was obtained by quantizing the weights and activations of unsloth/Mistral-Small-3.2-24B-Instruct-2506 to FP4 data type, ready for inference with vLLM>=0.9.1 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 ↗Mixtral-8x22B-Instruct-v0.1-FP8
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查看详情 ↗Mixtral-8x7B-Instruct-v0.1
RedHatAIThis model can be deployed efficiently on vLLM, Red Hat Enterprise Linux AI, and Openshift AI, as shown in the example below. > [!TIP] > PRs to correct the transformers tokenizer so that it gives 1-to-1 the same results as the mistral-common reference implementation are very welcome! --- The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks we tested.
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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。