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A **family of nine GGUF files** of `Qwen3.8-27B` (the native vision-language 27B dense model, Gated DeltaNet + Gated Attention hybrid layout, 262,144-token native context, MTP speculative head), converted from unsloth/Qwen3.8-27B-NVFP4. The **MTP (multi-token prediction) speculative head is baked into every file** — no separate drafter needed. - **`ORIG`** — the source-preserving conversion: native **NVFP4 MLP backbone** + **BF16 attention/embeddings** (the source's F8 attention is dequantized to BF16 because GGML has no F8 tensor type). This is the largest file and the one all tiers are derived from. - **`VERY-LOW` / `COMPACT-LOW` / `LOW` / `MEDIUM` / `MID-HIGH` / `HIGH` / `VERY-HIGH`** — a compact family sharing a **byte-identical 448-tensor NVFP4 backbone** (all attention + MLP re-quantized to NVFP4), differing only in the 10 "extra" tensors (LM head, token embedding, MTP draft head). `COMPACT-LOW` fills the gap between `VERY-LOW` and `LOW` — slightly smaller than `LOW` while keeping a materially stronger LM head than `VERY-LOW` (Q4_K vs Q3_K). `MID-HIGH` sits between `MEDIUM` and `HIGH` with all three head groups at **Q8_0**. - **`HIGHEST`** — the top tier: keeps the source's native **NVFP4 MLP** (layers 0-55) exactly as in `ORIG`, restores **Q8_0** for attention + the late MLP layers + the LM head, and keeps the **token embedding + MTP head in BF16**. The closest compact approximation of the source layout, for high-end GPUs. The goal: **keep native NVFP4 density across the whole model for Blackwell**, and offer a size/precision ladder for the tensors that most affect output quality and decode speed. On our dual 16 GB Blackwell setup every tier fits and runs (see notes before treating any numbers as meaningful).

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**HF repos:** `esatapedico/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-NVFP4` (safetensors, compressed-tensors NVFP4) and `esatapedico/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-NVFP4-GGUF` (this GGUF family, 8 tiers). Budget no-MTP variants (BUDGET 14.72, STARVED 14.59 GB, `Q3_K`/`Q2_K` heads, 1,107 tensors, `block_count` 64) live in the companion repo `esatapedico/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU-NVFP4-BUDGET-GGUF`. A **family of eight GGUF files** of DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU, DavidAU's TURBO 735-882 tune (Fable plus Cold Fusion plus Heretic/Uncensored, 735/882 Heretic merge and DPO layers) on Qwen3.8-27B: a 27B dense hybrid model (Gated DeltaNet plus Gated Attention every fourth layer, 262K native context, embedded MTP speculative head, native vision tower). NVFP4 here means W4A16 with FP8 scales, group size 16, weight only. Linear layers are NVFP4, vision tower, linear attention path, lm_head, embeddings and MTP head are kept in BF16 at the quantization source. The **MTP head is baked into every file in this repo**, no separate drafter is needed (`--spec-type draft-mtp`). My part here is only the numerics: I converted the NVFP4 checkpoint to GGUF and built a size and precision ladder for the tensors that most affect output quality and decode speed. All credit for the model itself belongs upstream (full chain below).

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**HF repos:** `esatapedico/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-NVFP4` (safetensors, compressed-tensors NVFP4) and `esatapedico/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-NVFP4-GGUF` (this GGUF family, 6 tiers). A **family of six GGUF files** of DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored, DavidAU's TWIN-TURBO tune (Fable plus Cold Fusion plus Heretic/Uncensored on Qwen3.8-27B, tuned for sharply reduced thinking tokens at matched quality): a 27B dense hybrid model (Gated DeltaNet plus Gated Attention every fourth layer, 262K native context, embedded MTP speculative head, native vision tower). NVFP4 here means W4A16 with FP8 scales, group size 16, weight only. Linear layers are NVFP4, vision tower, linear attention path, lm_head, embeddings and MTP head are kept in BF16 at the quantization source. The **MTP head is baked into every file in this repo**, no separate drafter is needed (`--spec-type draft-mtp`). My part here is only the numerics: I converted the NVFP4 checkpoint to GGUF and built a size and precision ladder for the tensors that most affect output quality and decode speed. All credit for the model itself belongs upstream (full chain below).

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Based on this paper, this is the SocRoBERTa-social language model. A language model that is trained to better classify social texts in the ESG domain. *Note: We generally recommend choosing the SocialBERT-social model since it is quicker, less resource-intensive and only marginally worse in performance.* Using the SocRoBERTa-base model as a starting point, the SocRoBERTa-social Language Model is additionally fine-trained on a 2k social dataset to detect social text samples.

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文本分析
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
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来源 / 文档 ↗
COST EXPLORER

好模型，也要算好成本。

费用分析

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

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

选型与常见问题
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。

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