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AI モデル 84759

• kanana-1.5-8b-instruct-2505-Safe-DPO↗

  Taewan123

• TeraTTSv2↗

  TeraSpace

  TeraTTSv2 is a self-contained ONNX Runtime text-to-speech release with selectable diffusion samplers, ten voice styles, Russian stress marking, and streamed audio output. This release uses the clean English/Russian 25-second teacher and its matching eight-step CFG-3 distilled student. > **Important — Russian stress is automatic.** Text inside ` … ` receives > stress markers automatically by default. Explicit `+` markers always win. > > **Important — cross-language prompts.** When an English reference voice is > speaking Russian, experiment with `duration_scale` below `1` (for example > `0.8`). It is usually a better starting point than the default `1`. > > **Recommended voices:** `ru_f1` and `ru_m5` are the preferred Russian voice > prompts.

• CodeLlama-70B-hf-GGUF↗

  TheBloke

• TinyLlama-1.1B-Chat-v0.3-GPTQ↗

  TheBloke

• NVIDIA-Nemotron-3.5-Bolt-74B-A9B-MLX↗

  thelogicalgate

• hrnet_w18_small_v2.ms_in1k↗

  timm

• NeoHorse-Jev-4B-GGUF↗

  TokenRhythm

• Qwen2.5-1.5B-bnb-4bit↗

  unsloth

  We have a Qwen 2.5 (all model sizes) free Google Colab Tesla T4 notebook. Also a Qwen 2.5 conversational style notebook. All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:

• bge-m3-korean↗

  upskyy

  This model is korsts and kornli finetuning model from BAAI/bge-m3. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. - **Model Type:** Sentence Transformer - **Base model:** BAAI/bge-m3 - **Maximum Sequence Length:** **8192 tokens** - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

• clip-vit-large-patch14-336↗

  Xenova