HSToric_Color_Wan2.2_5B_LoRA_BySilverAgePoets
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 84853
Jodorowsky_Wan_14b_LoRA_BySilverAgePoets
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查看详情 ↗StabledHSTorY_SD3.5_LoRA_V1
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查看详情 ↗YegorLetovVar3_FluxLoRA_BySilverAgePoets
AlekseyCalvin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。
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查看详情 ↗h3-comfy-video
aleksyuk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。
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查看详情 ↗Qwen3.6-27B-AWQ-4bit
AlennnndyThis repository contains an optimized, 4-bit quantized version of the original Qwen3.6-27B model by Alibaba Cloud. The quantization was performed using the **AWQ** algorithm via the `llmcompressor` library, drastically reducing the VRAM required for local inference without a perceptible degradation in the original model's accuracy. * **Base Model:** `Qwen/Qwen3.6-27B` * **Algorithm:** AWQ * **Scheme:** W4A16_ASYM (4-bit asymmetric weights, 16-bit activations) * **Ignored Modules:** `lm_head`, `visual`, `vision` (Vision encoder layers are kept in their original precision to maintain multimodal capabilities). * **Weights Format:** Safetensors * **Calibration Dataset:** `HuggingFaceH4/ultrachat_200k` (512 random samples from the `train_sft` split, formatted with the model's chat template. Max sequence length: 1024).
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查看详情 ↗Kolibri-1
Aleph-AlphaKolibri is Aleph Alpha's mixture-of-experts (MoE) reasoning model, with a focus on German and English. The model supports an explicit reasoning mode and tool calling. It is optimized for long-context and inference efficiency. Kolibri is intended to process text input and output in German and English and to perform a wide range of tasks beyond natural-language generation, for example multi-step reasoning, coding, structured extraction, retrieval-augmented generation, long-document processing and agentic tool calling. Kolibri was pre-trained on sequences of 16,384 tokens, mid-trained on 65,536 and trained on 262,144 tokens in a final long-context phase, which is its native context length. Because positional encoding is applied only in the sliding-window layers, the context can be extended beyond that length without any position scaling, in principle to arbitrary lengths. We have validated quality and serving efficiency up to 1,048,576 tokens. For latency- or throughput-sensitive deployments and for complex tasks, we recommend contexts of at most 262,144 tokens. See the technical report for details.
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查看详情 ↗Kolibri-1-BF16
Aleph-Alpha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。
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查看详情 ↗Pharia-1-LLM-7B-control-hf
Aleph-AlphaThis is the safetensors-conversion of `Pharia-1-LLM-7B-control`. We provide a joint model card for `Pharia-1-LLM-7B-control` and `Pharia-1-LLM-control-aligned`. Find this model card here.
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查看详情 ↗FormulaNet
alephpi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。
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查看详情 ↗wav2vec2-cv-be
alesFine-tuned version of facebook/wav2vec2-base on `mozilla-foundation/common_voice_8_0 be` dataset. `Train`, `Dev`, `Test` splits were used as they are present in the dataset. No additional data was used from `Validated` split, only 1 voicing of each sentence was used - the way the data was split by CommonVoice CorporaCreator. To build a better model **one can use additional voicings from `Validated` split** for sentences already present in `Train`, `Dev`, `Test` splits, i.e. enlarge mentioned splits. Language model was built using KenLM. 5-gram Language model was built on sentences from `Train + (Other - Dev - Test)` splits of `mozilla-foundation/common_voice_8_0 be` dataset.
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ales27pm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。
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查看详情 ↗lumen-qwen3-bootstrap-gguf
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查看详情 ↗GLM-5.3-Flash-abliterated-Maya-M-GGUF
alesha-proProject Maya's Maya-M quant of GLM-5.3-Flash plus a 2.2 MB vector that removes refusals while the model runs, and a small patch for the Maya engine that applies it. On 4x RTX 3090 it decodes 56 tokens/s at a 256K context with the vector on. The model weights in this repo are not mine. They are Maya-M and the vision files of Project Maya, copied byte for byte from peasantsmith/GLM-5.3-Flash-Maya-GGUF at revision `3e9f3bb20aa191e01c6fb850faf132d8149da98a`. The SHA256 of all 5 files match theirs at that revision. See `SHA256SUMS`. The authors changed the first shard after I downloaded it. Their commit `1084950a` renames the architecture in the GGUF header to `glm5-next`, and the file size is the same. This repo has the earlier first shard, the one all my runs used with Maya v1.0.27. For the current files go to their repo.
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查看详情 ↗Qwen3.8-27B-S-mirai-GGUF
alesha-proA GGUF of Mirai's 2.4-bit Qwen3.8-27B. Mirai's compressed trellis codes are copied into the GGUF bit for bit, nothing is re-quantized. A 27B model in 11.2 GB that runs on one 12 GB GPU, with tools, reasoning, images, and an optional file that removes refusals. The format adds 4 ggml types, so mainline llama.cpp, LM Studio and Ollama will not load this file. 2 engines do. This is a community conversion, not an official Mirai release. Use mirai-s-ada. It took the codec from my fork, checked that its greedy output matches the fork token for token, and added what a 12 GB card needs: a KV cache that continues in system RAM, MTP drafting from the draft block inside this GGUF, and fixes for apps that send `effort: "high"` or tiny output caps. The numbers in its column are Cary's, from his RTX 4070 12 GB. I repeated them on Ubuntu with a 3090, see "Measured".
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查看详情 ↗The model weights in this repo are not mine. They are the GSQ-RCO Coder release of Qwen3.8-Flash-Next made by the Deep Algorithms and Systems Lab at ISTA: 256 of the 512 experts in each layer are kept, the rest are removed, and the kept weights are stored at 3.5 bits. The files are copied byte for byte from ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF at revision `5348543e0147355ac9cbcb031184a3546350988e`. The SHA256 of both shards and of the vision projector match their files. See `SHA256SUMS`. If you use these weights, cite the two ISTA-DASLab papers. The BibTeX is at the bottom of this page. My part is the check that the refusal vector I published for the unpruned sizes also works on the pruned model, and the measurements below. The vector is the same 480 KB file as in the repo with the four unpruned sizes, with the same SHA256. For the original card and ISTA's own SWE-bench and LiveCodeBench numbers, go to the ISTA-DASLab repo.
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查看详情 ↗The model weights in this repo are not mine. They are the four GSQ-RCO quantizations of Qwen3.8-Flash-Next (Q2_0, IQ2_XS, IQ3_XXS, IQ3_S) made by the Deep Algorithms and Systems Lab at ISTA, copied byte for byte from ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF at revision `ed59f92082b1e93c0e96d60a8b11aab089b52f09`. The SHA256 of every shard and of the vision projector match their files. See `SHA256SUMS`. If you use these weights, cite the two ISTA-DASLab papers. The BibTeX is at the bottom of this page. My part is one 480 KB file, `Huihui-Qwen3.8-Flash-Next-refusal-direction-r.gguf`, and the measurements below. The same file serves every size. I measured IQ3_S and IQ2_XS with it. Q2_0 and IQ3_XXS are here as ISTA-DASLab published them and I have not run them. For the original card and ISTA's own benchmarks, go to the ISTA-DASLab repo.
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查看详情 ↗Mirai's 2-bit trellis build of Qwen3.8-Flash-Next, packed for Strata, with a refusal vector and everything else it needs to run. It runs on one or two RTX 3090s with the experts in system RAM. `S/` holds Mirai's files byte for byte (the SHA256 match their repo). My part: the S-small GGUF (converted with Mirai's converter from their S-small trellis files), the refusal vector, the packaged MTP layer, the port of Mirai's Strata patch to Strata 0.2.0 (alesha-pro/Strata, upstream as Niko1221/Strata#3384), and the measurements below. The routed experts take 1 to 5 bits each, 2.4 on average for S. The second file of each size is the backbone and the head as trellis, which leaves more VRAM for experts. The quality numbers are Mirai's (37,620 positions against the bf16 model, measured outside Strata); for comparison they give 0.303 for ISTA's GSQ-RCO IQ2_XS. I did not check them.
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查看详情 ↗DeepSeek-V4-Flash-DSpark-Drafter-GGUF
alessandrobologna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。
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查看详情 ↗deimv2-deimv2_dinov3_x_coco
alessioarcara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。
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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。