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AI 模型 84759
- Whittle-Qwen-3.8-35B-A3B-GGUF↗
mradermacher
- Qwen3-0.6B-abliterated-GGUF↗
Mungert
- SENTIMENTAL_SEX-3.2-1B↗
Novaciano
- stt_en_conformer_ctc_large↗
NVIDIA
This model transcribes speech in lowercase English alphabet including spaces and apostrophes, and is trained on several thousand hours of English speech data. It is a non-autoregressive "large" variant of Conformer, with around 120 million parameters. See the model architecture section and NeMo documentation for complete architecture details. It is also compatible with NVIDIA Riva for production-grade server deployments. The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. To train, fine-tune or play with the model you will need to install NVIDIA NeMo. We recommend you install it after you've installed latest PyTorch version.
- Bonsai-1.7B-ONNX↗
onnx-community
- VisualPRM-8B-v1_1↗
OpenGVLab
- tiny-random-Starcoder2ForCausalLM↗
optimum-intel-internal-testing
- Llama-3.2-1B↗
osllmai-community
**We believe there's no need to pay a token if you have a GPU on your computer.** Highlighting new and noteworthy models from the community. Join the conversation on Discord.
- plamo-2.1-2b-vl↗
pfnet
PLaMo 2.1-2B-VL is a vision-language model developed by Preferred Networks, Inc., based on the instruction-tuned PLaMo 2.1-2B and trained on English and Japanese datasets. While the 8B model (pfnet/plamo-2.1-8b-vl) will serve as the main offering, supported by performance comparisons, this smaller and lighter 2B model is made available as an easy option to try, although its use cases are more limited. The development of PLaMo 2.1-2B-VL was focused on creating a model for usage on autonomous devices such as drones, robots, vehicles, and surveillance cameras, aiming to create a high-accuracy, lightweight VLM that runs efficiently on edge devices.
- LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS↗
PhillyLabs
**This is LiquidAI's LFM2.5-2.6B with its refusal behavior aggressively reduced.** It keeps the compact 2.7B hybrid architecture, but answers requests the stock post-trained model refused. In our tests, refusals fell from 132/842 to 8/842 and from 19/126 to 1/126 on a held-out set, while the average change across five public capability benchmarks was only -0.50 percentage points. **PHILADELPHIA CLASS** includes the full BF16 checkpoint as one `model.safetensors` file plus a tested BF16 GGUF for llama.cpp. Both retain LFM2.5's tokenizer, reasoning chat template, and 128K configured context. This repository redistributes modified model weight files derived from `LiquidAI/LFM2.5-2.6B` at revision `dca1825886789bd40b94368f53b1d9ada4c94598`. The local untouched checkpoint was verified against all 11 files in that Hub revision before release preparation.