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AI Models 84759

• Yi-1.5-34B-Chat↗

  01-ai

  Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. Model | Context Length | Pre-trained Tokens | :------------: | :------------: | :------------: | | Yi-1.5 | 4K, 16K, 32K | 3.6T

• extBanglaT5↗

  101rakibulhasan

• Hy-MT2-7B-Abliterated-GGUF↗

  alexwkleung

  GGUF conversions of `mlx-community/Hy-MT2-7B-Abliterated-bf16`, an abliterated build of Tencent's `Hy-MT2-7B` translation model. The abliteration existed only in MLX format, which means Apple Silicon and `mlx_lm` only. Every abliterated Hy-MT2 GGUF published so far is the **1.8B**; every abliterated **7B** was MLX-only. > **Modification notice** (Apache-2.0 §4(b)). These files are quantized GGUF conversions of an > abliterated derivative of `tencent/Hy-MT2-7B` — the refusal-direction ablation was performed by > mlx-community, the format > conversion here. **Neither Tencent nor the ablation author produced, endorsed, or reviewed this > repo**, and its behavior is not theirs.

• Simulation_LLM_google_7B_V2↗

  Alibaba-NLP

• Qwen3.8-27B-ABLITERATED-3bit-MLX-TextOnly↗

  ap3212

• tiny-llama3-test↗

  ariG23498

• RoboBrain-LoRA-Affordance↗

  BAAI

• finance-sentiment-ja-base↗

  bardsai

• Yi-1.5-6B-Chat-GGUF↗

  bartowski

  If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

• bloom-1b1↗

  bigscience

  BLOOM LM BigScience Large Open-science Open-access Multilingual Language Model Model Card 1. Model Details 2. Uses 3. Training Data 4. Risks and Limitations 5. Evaluation 6. Recommendations 7. Glossary and Calculations 8. More Information 9. Model Card Authors *This section provides information for anyone who wants to know about the model.*