Open-Weight Models

This model is a fine-tuned version of google/gemma-3-4b-it. It has been trained using TRL. This model was trained with SFT.

This model is a fine-tuned version of google/gemma-3-4b-it. It has been trained using TRL. This model was trained with SFT.

This model is a fine-tuned version of google/gemma-3-4b-it. It has been trained using TRL. This model was trained with SFT.

This model is a fine-tuned version of google/gemma-3-4b-it. It has been trained using TRL. This model was trained with SFT.

This model is a fine-tuned version of google/gemma-3-4b-it. It has been trained using TRL. This model was trained with SFT.

This model is a fine-tuned version of google/gemma-3-4b-it. It has been trained using TRL. This model was trained with SFT.

This model is a fine-tuned version of google/gemma-3-4b-it. It has been trained using TRL. This model was trained with SFT.

This model is a fine-tuned version of google/gemma-3-4b-it. It has been trained using TRL. This model was trained with SFT.

This model is a fine-tuned version of google/gemma-3-4b-it. It has been trained using TRL. This model was trained with SFT.

This model is a fine-tuned version of google/gemma-3-4b-it. It has been trained using TRL. This model was trained with SFT.

This model is a fine-tuned version of google/gemma-3-4b-it. It has been trained using TRL. This model was trained with SFT.

This model is a fine-tuned version of google/gemma-3-4b-it. It has been trained using TRL. This model was trained with SFT.

On a laptop running Debian 13 Linux, with an Intel Core Ultra 9 185H, its integrated Arc GPU and 32 GB of memory, Janas-LLM reads a prompt at 65 tok/s and writes at 9.9 tok/s, 14 tok/s with its own assistant in a chat: llama.cpp, on the same laptop, 46 and 7.1. Measured on 9-11 October 2026: llama-bench -p 1024 -n 16 and janas-bench --prompt 1024 --gen 16, both at their best configuration; the drafts with janas-bench on a chat reply, the mean of three rounds (a draft guesses the next tokens and the model checks them: the same text, faster). On other machines the figures will differ. Details: Janas README. - Debian 13 (trixie) Linux, x86-64, kernel 6.12.107 - its integrated Intel Arc GPU…

Model · Text generation

gemma-4-12B-it-Pollard

Pollard Weights

Pollard builds of google/gemma-4-12B-it made with Pollard Weights -- a ladder of measured-allocation quants (bits placed by per-layer sensitivity, not a uniform crush). Standard GGUF -- every file here runs in stock llama.cpp / ikllama.cpp, Ollama, LM Studio. Every rung is the same weights, sized to a different RAM budget by the measured allocation. Pick the largest one that fits your machine with room for context: - ~12 GB RAM / VRAM -> Q6K (9.65 GB). - ~9 GB RAM / VRAM -> IQ4XS (6.78 GB). - ~7 GB RAM / VRAM -> IQ2XXS (4.64 GB). f16 reference PPL 23.041768. These are standard GGUF and run with llama.cpp: They also work in anything built on llama.cpp -- LM Studio, koboldcpp, Jan, ramalama…

Google publishes the QAT Q40 model google/gemma-4-12B-it-qat-q40-gguf as GGUF, but its multi-token-prediction (MTP) assistant only as safetensors There was no GGUF MTP head for the QAT GGUF, so this repository is that assistant converted to GGUF, nothing else: no training or fine-tuning. Use it together with google/gemma-4-12B-it-qat-q40-gguf for speculative decoding in llama.cpp. The Google model card asks for exactly this pairing: "When using multi-token prediction (speculative decoding) with an assistant model alongside a QAT target model, the assistant model must also be a QAT checkpoint with the same precision." llama.cpp at commit 0c1e570 (Gemma4UnifiedAssistantForCausalLM → GGUF…

Model · Text generation

gemma-4-12B-it-qat-q4_0-unquantized-MoE

Aifeifei

Inspired by the big.LITTLE architecture in phone SoCs — a few big cores for heavy lifting, many LITTLE cores for efficient background work, and a scheduler that migrates tasks by demand — Myriad-MoE applies the same idea to LLM adapters. Grafted onto a frozen Gemma-4-12B backbone (48 layers, work, 16 clans × 16 LITTLE micro-experts absorb fine-grained load, and 1 always-on shared core holds global context like a system housekeeping core. Learned routers play scheduler; training uses supervised domain routing, 4-bit QLoRA-style adapters, and a hot-swappable patch workflow. Ships with a FastAPI backend and a pnpm (Vite + React) chat UI with a live routing probe. MoE adapters are grafted onto…

LoRA adapters for google/gemma-4-12B-it trained with supervised fine-tuning (SFT) on visual questions generated by PixelProof, as described in the paper PixelProof: Visual Question Generation through Forward–Inverse Agreement. In PixelProof, a coding agent writes a forward program that draws images and computes their answers and an inverse program that must recover each answer from the image alone; a question is kept only if the two agree. Each adapter is stored in its own subfolder. The repository contains no merged weights. Training uses transformers.Trainer and PEFT LoRA adapters on the language model's attention and feed-forward projections; the vision tower is not directly adapted. The…

基于 google/gemma-4-12B-it 的 LoRA 适配器。使用经预处理和筛选的六个公开数据来源，以 RegMix config-025 配比组成 50,000 条训练数据；不包含 Lusy，也不以 Lusy 为筛选或优化目标。 row% 为样本数占比，target token 为按本次 Gemma tokenizer 和训练监督区间统计的单轮数据集监督 token 总数，tgt% 为其占比；中位数按逐条样本的监督 token 数计算，合计行为全部 50,000 条的中位数。百分比四舍五入。训练共 2 个 epoch；表中不重复计数。不同基座 tokenizer 下的 token 数不能与 v1 直接等同比较。 六个来源分别为 CoSER、XPersona、Aya、Tulu-3-SFT-Mixture、SmolTalk 和 Infinity-Instruct。混合数据包含 50,000 个唯一 ID、50,000 个唯一内容哈希，与留出集的分组重叠数为 0。 训练集 SHA-256：8c626b338270652de2e4d1130b28d8b0fc2c2eca0b90f5dcffa7580a26e0d859。 配比来自 64 个 Gemma-E2B 代理配置及候选确认实验。此版本发布已完成的 config-025 正式训练产物；不将其宣称为 Gemma 12B 上已证明的全局最佳配比。 本仓库包含 LoRA 权重、适配器配置、tokenizer 和 chat template；不包含合并后的基座权重。加载时使用 google/gemma-4-12B-it…

This repository contains an MXFP8-weight checkpoint derived from exported in compressed-tensors format. The checkpoint retains the source multimodal components, but the evaluation reported here covers text tasks only. The exported checkpoint contains 11,635 F8E4M3 weight tensors, 838 BF16 weight tensors, 11,635 U8 block-scale tensors, and 171 FP32 scale/metadata tensors. Routed-expert weights and source-present text self-attention projections are MXFP8; the router, shared MLP, vision tower, and embeddings remain BF16. Measured on 2026-09-30 with lm-eval 0.4.13 and vLLM 0.29.0. The tested settings were TRITONATTN, tensor parallelism 2, pipeline parallelism 1, batch size 64, maxnumseqs=64…