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AI モデル 85004
- smolpotato_mutekiflame-flux-v1↗
glif-loradex-trainer
- paligemma-3b-pt-448-jax↗
Google
- t5-11b-ssm-nq↗
Google
- SmolLM-1.7B↗
HuggingFaceTB
1. Model Summary 2. Limitations 3. Training 4. License 5. Citation SmolLM is a series of state-of-the-art small language models available in three sizes: 135M, 360M, and 1.7B parameters. These models are built on Cosmo-Corpus, a meticulously curated high-quality training dataset. Cosmo-Corpus includes Cosmopedia v2 (28B tokens of synthetic textbooks and stories generated by Mixtral), Python-Edu (4B tokens of educational Python samples from The Stack), and FineWeb-Edu (220B tokens of deduplicated educational web samples from FineWeb). SmolLM models have shown promising results when compared to other models in their size categories across various benchmarks testing common sense reasoning and world knowledge. For detailed information on training, benchmarks and performance, please refer to our full blog post. While SmolLM models have been trained on a diverse dataset including educational content and synthetic texts, they have limitations. The models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. For a more comprehensive discussion of the models' capabilities and limitations, please refer to our full blog post.
- LoopedLM-P1-moe-middle-input-injection-500b↗
IFM
- google_gemma-4-E2B-it-GGUF↗
indiansatoshi
- spatialvla-4b-224-pt↗
IPEC-COMMUNITY
- Qwen3.8-3.6-27B-blend↗
JetBrains
- text-seg-lm-qwen2-0.5b-cot-topic-chunking↗
jinaai
- UNSEEN_Gemma_4_26B_NSFW-GGUF↗
Jommarn
> 📌 **Base Model:** The full unquantized FP16/BF16 weights are hosted at 👉 **Jommarn/UNSEEN_Gemma_4_26B_NSFW** (surgically abliterated from `google/gemma-4-26B-A4B-it`). > > ⚡ **This Repository:** Dedicated to optimized **Quantized (2-bit / 3-bit / 4-bit / 5-bit / 6-bit / 8-bit / 16-bit Master)** and **GGUF** deployment formats for consumer GPUs, Apple Silicon Mac, and `llama.cpp` serving. - **2-bit (`IQ2_M` ~9.67GB):** ~8–10GB VRAM (e.g., RTX 3060 12GB, RTX 4060, Mac 16GB) — *Extreme Efficiency* - **3-bit (`IQ3_M` ~12.4GB):** ~12–14GB VRAM (e.g., RTX 4070 12GB, RTX 3080, Mac 16GB) — *High Efficiency* - **4-bit (`Q4_K_M` ~15.6GB / `NF4`):** ~16–18GB VRAM (e.g., RTX 3090, RTX 4090, NVIDIA L4) — *Recommended Standard* - **5-bit (`Q5_K_M` ~18.3GB):** ~20–22GB VRAM (e.g., RTX 3090/4090 24GB, A10G 24GB) — *Balanced High-Quality* - **6-bit (`Q6_K` ~22.6GB):** ~24–26GB VRAM (e.g., RTX 4090 24GB, A100 40GB) — *High Precision* - **8-bit (`Q8_0` ~28.0GB / `INT8`):** ~28–30GB VRAM (e.g., 2x RTX 3090/4090, A100 40GB/80GB) — *99.9% Lossless* - **16-bit (`BF16` ~50.5GB):** ~52GB VRAM (e.g., A100 80GB, Mac Studio 64GB/128GB) — *100% Uncompressed Master* - **Vision Projector (16-bit):** `mmproj-gemma4-vision-f16.gguf` (~1.11GB) — *Must be loaded with `--mmproj` for image perception in llama.cpp/GGUF.* - **GGUF Weights:** Available at 👉 **Jommarn/UNSEEN_Gemma_4_26B_NSFW-GGUF** > ⚠️ **Important for GGUF Users:** To enable image understanding in `llama.cpp` or LM Studio, you **MUST** download and pass `mmproj-gemma4-vision-f16.gguf` alongside the main model. Without it, the model will operate strictly as a text-only LLM!