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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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