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gemmable-4-12b-mtp
## Gemmable 4 12B Gemmable 4 12B is a GGUF export of Gemma 4 12B fine-tuned on Fable-5 style reasoning and assistant traces. ## Highlights - Base model: `google/gemma-4-12B` - Format: GGUF - Training style: Fable-5 style reasoning and assistant traces - Distribution: fp16 GGUF plus matching assistant GGUFs for each quant - Intended use: local inference, coding, reasoning, and assistant workflows ## How to use ### llama.cpp Standard load: ```bash llama-server -m "gemmable-4-12b-fp16.gguf" ``` Speculative / draft-MTP load: ```bash llama-server -m "gemmable-4-12b-Q4_K_M.gguf" \ --spec-draft-model "gemmable-4-12b-Q4_K_M-mtp.gguf" \ --spec-type draft-mtp \ --spec-draft-n-max 4 ``` Use the matching fp16 or quantized main file with its `-mtp` companion. ### LM Studio 1. Search this repo, download target + mtp file. 2. Load target. 3. Load settings → Speculative Decoding → select mtp file file. (Requires a llama.cpp runtime with Gemma 4 MTP support from ggml-org/llama.cpp#23398. LocalAI's pinned llama.cpp backend already carries it, so this entry runs draft-mtp out of the box.) ## GGUF / local inference notes ...

Repository: localai

qwen3-4b-dflash
Qwen3-4B paired with its DFlash block-diffusion drafter for speculative decoding on the llama.cpp backend. This is the canonical DFlash pairing documented upstream (`z-lab/Qwen3-4B-DFlash` + `Qwen/Qwen3-4B`). DFlash produces a whole block of draft tokens in a single forward pass and injects the target model's hidden states into the drafter's attention, which keeps the drafter tiny while making drafting GPU-friendly. The Q4_K_M file carries the full Qwen3-4B target; the ~0.5 GB Q8_0 drafter (`draft-dflash`) accelerates generation without changing the target's outputs. The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here. Flash attention is required for DFlash and is enabled in this config. A GPU is recommended. License: Apache 2.0 (Qwen3-4B target) / MIT (z-lab DFlash drafter).

Repository: localaiLicense: apache-2.0

qwen3.5-4b-dflash
Qwen3.5-4B paired with its DFlash block-diffusion drafter for speculative decoding on the llama.cpp backend. DFlash produces a whole block of draft tokens in a single forward pass and injects the target model's hidden states into the drafter's attention, which keeps the drafter tiny while making drafting GPU-friendly. The Q4_K_M file carries the full Qwen3.5-4B target; the ~0.6 GB Q8_0 drafter (`draft-dflash`) accelerates generation without changing the target's outputs. The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here. Flash attention is required for DFlash and is enabled in this config. A GPU is recommended. License: Apache 2.0 (Qwen3.5-4B target) / MIT (z-lab DFlash drafter).

Repository: localaiLicense: apache-2.0

qwen3.5-9b-dflash
Qwen3.5-9B paired with its DFlash block-diffusion drafter for speculative decoding on the llama.cpp backend. DFlash produces a whole block of draft tokens in a single forward pass and injects the target model's hidden states into the drafter's attention, which keeps the drafter tiny while making drafting GPU-friendly. The Q4_K_M file carries the full Qwen3.5-9B target; the ~1 GB Q8_0 drafter (`draft-dflash`) accelerates generation without changing the target's outputs. The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here. Flash attention is required for DFlash and is enabled in this config. A GPU is recommended. License: Apache 2.0 (Qwen3.5-9B target) / MIT (z-lab DFlash drafter).

Repository: localaiLicense: apache-2.0

qwen3.6-27b-dflash
Qwen3.6-27B (dense) paired with its DFlash block-diffusion drafter for speculative decoding on the llama.cpp backend. DFlash gives its largest speedups on dense targets like this one. DFlash produces a whole block of draft tokens in a single forward pass and injects the target model's hidden states into the drafter's attention, which keeps the drafter tiny while making drafting GPU-friendly. The Q4_K_M file carries the full Qwen3.6-27B target; the ~1.8 GB Q8_0 drafter (`draft-dflash`) accelerates generation without changing the target's outputs. The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here. Flash attention is required for DFlash and is enabled in this config. A GPU is recommended. License: Apache 2.0.

Repository: localaiLicense: apache-2.0

qwen3.6-35b-a3b-dflash
Qwen3.6-35B-A3B (Mixture-of-Experts, ~3B active per token) paired with its DFlash block-diffusion drafter for speculative decoding on the llama.cpp backend. DFlash speedups on MoE targets are smaller than on dense models, but still useful. DFlash produces a whole block of draft tokens in a single forward pass and injects the target model's hidden states into the drafter's attention, which keeps the drafter tiny while making drafting GPU-friendly. The UD-Q4_K_M file carries the full Qwen3.6-35B-A3B target; the ~0.4 GB Q8_0 drafter (`draft-dflash`) accelerates generation without changing the target's outputs. The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here. Flash attention is required for DFlash and is enabled in this config. A GPU is recommended. License: Apache 2.0.

Repository: localaiLicense: apache-2.0

gemma-4-26b-a4b-it-qat
Hugging Face | GitHub | Launch Blog | Documentation License: Apache 2.0 | Authors: Google DeepMind > [!Note] > This model card is for the new versions of the Gemma 4 family optimized with Quantization-Aware Training (QAT), which allows preserving similar quality to bfloat16 while dramatically reducing the memory requirements to load the model. > Four versions of the QAT checkpoints are available: > * **Unquantized QAT checkpoints** (Q4_0): Half-precision weights extracted from the QAT pipeline, ideal for custom downstream compilation and research. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B, and their drafter models. > * **GGUF** (Q4_0): Ready-to-deploy formats for broad ecosystem compatibility. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B. > * **Mobile-optimized** (wNa8o8): A custom schema engineered explicitly for mobile hardware efficiency. It features targeted 2-bit decoding layers, optimized KV caches, and static activations to maximize VRAM savings. Available for Gemma 4 E2B and E4B. > * **Compressed Tensors** (w4a16): QAT checkpoints serialized in the compressed-tensors format for native, optimized inference with vLLM. Available for Gemma 4 E2B, E4B, 12B ...

Repository: localaiLicense: apache-2.0

gemma-4-12b-it-qat-q4_0
Hugging Face | GitHub | Launch Blog | Documentation License: Apache 2.0 | Authors: Google DeepMind > [!Note] > This model card is for the new versions of the Gemma 4 family optimized with Quantization-Aware Training (QAT), which allows preserving similar quality to bfloat16 while dramatically reducing the memory requirements to load the model. > Four versions of the QAT checkpoints are available: > * **Unquantized QAT checkpoints** (Q4_0): Half-precision weights extracted from the QAT pipeline, ideal for custom downstream compilation and research. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B, and their drafter models. > * **GGUF** (Q4_0): Ready-to-deploy formats for broad ecosystem compatibility. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B. > * **Mobile-optimized** (wNa8o8): A custom schema engineered explicitly for mobile hardware efficiency. It features targeted 2-bit decoding layers, optimized KV caches, and static activations to maximize VRAM savings. Available for Gemma 4 E2B and E4B. > * **Compressed Tensors** (w4a16): QAT checkpoints serialized in the compressed-tensors format for native, optimized inference with vLLM. Available for Gemma 4 E2B, E4B, 12B ...

Repository: localaiLicense: apache-2.0

gemma-4-e2b-it-qat-mtp
Gemma 4 E2B IT QAT (Google DeepMind) paired with its Multi-Token Prediction (MTP) drafter head for speculative decoding on the llama.cpp backend. The Q4_K_XL target carries the full multimodal (text + image) model; the small `mtp-gemma-4-E2B-it` head predicts several tokens ahead which the target verifies in parallel, accelerating generation with no change to output quality. E2B is a MatFormer "effective 2B" elastic variant, well suited to lightweight and on-device deployments. The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here. It uses the upstream `gemma4-assistant` architecture registered by llama.cpp PR #23398, so it loads on stock llama.cpp without any patch. License: Apache 2.0 | Authors: Google DeepMind (target/drafter checkpoints), Unsloth (GGUF conversion)

Repository: localaiLicense: apache-2.0

gemma-4-e4b-it-qat-mtp
Gemma 4 E4B IT QAT (Google DeepMind) paired with its Multi-Token Prediction (MTP) drafter head for speculative decoding on the llama.cpp backend. The Q4_K_XL target carries the full multimodal (text + image) model; the small `mtp-gemma-4-E4B-it` head predicts several tokens ahead which the target verifies in parallel, accelerating generation with no change to output quality. E4B is a MatFormer "effective 4B" elastic variant, balancing quality and footprint for on-device and edge deployments. The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here. It uses the upstream `gemma4-assistant` architecture registered by llama.cpp PR #23398, so it loads on stock llama.cpp without any patch. License: Apache 2.0 | Authors: Google DeepMind (target/drafter checkpoints), Unsloth (GGUF conversion)

Repository: localaiLicense: apache-2.0

gemma-4-12b-it-qat-mtp
Gemma 4 12B IT QAT (Google DeepMind) paired with its Multi-Token Prediction (MTP) drafter head for speculative decoding on the llama.cpp backend. The Q4_K_XL target carries the full multimodal (text + image) model; the small `mtp-gemma-4-12B-it` head predicts several tokens ahead which the target verifies in parallel, accelerating generation with no change to output quality. As a dense model, Gemma 4 12B is among the sizes that benefit most from MTP, with the llama.cpp PR reporting well over 1.4x decode speedup. The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here. It uses the upstream `gemma4-assistant` architecture registered by llama.cpp PR #23398, so it loads on stock llama.cpp without any patch. License: Apache 2.0 | Authors: Google DeepMind (target/drafter checkpoints), Unsloth (GGUF conversion)

Repository: localaiLicense: apache-2.0

gemma-4-31b-it-qat-mtp
Gemma 4 31B IT QAT (Google DeepMind), the largest dense model in the family, paired with its Multi-Token Prediction (MTP) drafter head for speculative decoding on the llama.cpp backend. The Q4_K_XL target carries the full multimodal (text + image) model; the small `mtp-gemma-4-31B-it` head predicts several tokens ahead which the target verifies in parallel, accelerating generation with no change to output quality. Dense models like 31B are the sizes that benefit most from MTP. The drafter is not a standalone chat model: it only runs paired with the target, which is why both are bundled here. It uses the upstream `gemma4-assistant` architecture registered by llama.cpp PR #23398, so it loads on stock llama.cpp without any patch. License: Apache 2.0 | Authors: Google DeepMind (target/drafter checkpoints), Unsloth (GGUF conversion)

Repository: localaiLicense: apache-2.0

alamios_mistral-small-3.1-draft-0.5b
This model is meant to be used as draft model for speculative decoding with mistralai/Mistral-Small-3.1-24B-Instruct-2503 or mistralai/Mistral-Small-24B-Instruct-2501 Data info The data are Mistral's outputs and includes all kind of tasks from various datasets in English, French, German, Spanish, Italian and Portuguese. It has been trained for 2 epochs on 20k unique examples, for a total of 12 million tokens per epoch.

Repository: localaiLicense: apache-2.0

gemma-4-e2b-it:sglang-mtp
Google Gemma 4 E2B-IT served by SGLang with Multi-Token Prediction (MTP) speculative decoding. The companion drafter google/gemma-4-E2B-it-assistant lets the target accept several tokens per step. Flags are a 1:1 transcription of the SGLang cookbook's MTP command (NEXTN algorithm, num_steps=5, num_draft_tokens=6, eagle_topk=1, mem_fraction_static=0.85). The E2B variant has 5B total / 2B effective parameters and targets the smaller end of consumer GPUs.

Repository: localaiLicense: gemma

gemma-4-e4b-it:sglang-mtp
Google Gemma 4 E4B-IT served by SGLang with Multi-Token Prediction (MTP) speculative decoding. The companion drafter google/gemma-4-E4B-it-assistant lets the target accept several tokens per step. Flags are a 1:1 transcription of the SGLang cookbook's MTP command (NEXTN algorithm, num_steps=5, num_draft_tokens=6, eagle_topk=1, mem_fraction_static=0.85). The E4B variant has 8B total / 4B effective parameters — the natural pick for consumer GPUs in the 16–24 GB range.

Repository: localaiLicense: gemma

mimo-7b-mtp:sglang
Xiaomi MiMo-7B-RL served by SGLang with built-in Multi-Token Prediction (MTP) heads (no separate drafter needed) plus online fp8 weight quantization to fit on a 16 GB consumer GPU. ~90% acceptance per the model card. Verified end-to-end at ~88 tok/s on an RTX 5070 Ti (16 GB). Note: mem_fraction_static is dropped to 0.7 (vs sglang's 0.85 default) because the MTP draft worker's vocab embedding is loaded unquantised (~1.2 GiB) and OOMs the static reservation otherwise.

Repository: localaiLicense: mit