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AI 모델 84759

• pixelartredmond-1-5v-pixel-art-loras-for-sd-1-5↗

  artificialguybr

• Infinity-Instruct-3M-0625-Yi-1.5-9B↗

  BAAI

  Infinity-Instruct-3M-0625-Yi-1.5-9B is an opensource supervised instruction tuning model without reinforcement learning from human feedback (RLHF). This model is just finetuned on Infinity-Instruct-3M and Infinity-Instruct-0625 and showing favorable results on AlpacaEval 2.0 and MT-Bench. Infinity-Instruct-3M-0625-Yi-1.5-9B is tuned on Million-level instruction dataset Infinity-Instruct. First, we apply the foundational dataset Infinity-Instruct-3M to improve the foundational ability (math & code) of Yi-1.5-9B, and get the foundational instruct model Infinity-Instruct-3M-Yi-1.5-9B. Then we finetune the Infinity-Instruct-3M-Yi-1.5-9B to get the stronger chat model Infinity-Instruct-3M-0625-Yi-1.5-9B. Here is the training hyperparamers. Thanks to FlagScale, we could concatenate multiple training samples to remove padding token and apply diverse acceleration techniques to the traning procudure. It effectively reduces our training costs. We will release our code in the near future!

• Llama-3-8B-Ultra-Instruct-GGUF↗

  backyardai

• bigstral-12b-32k-8xMoE-GGUF↗

  bartowski

• gemma-2-9b-it-abliterated-GGUF↗

  bartowski

  Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using. 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:

• nvidia_Nemotron-3-Nano-4B-GGUF↗

  bartowski

• tencent_UI-Mate-9B-GGUF↗

  bartowski

  **Model details:** - Parameter count: 9B - Input support: text, image (with mmproj file) - details - Speculative decoding: no - imatrix: yes - details - Perplexity/KLD measured: no **Don't know which to choose?** Grab Q4_K_M (5.91GB) - usually a good mix of size and performance. Download instructions available here These quants were made with llama.cpp release b10472 - if this model's architecture is newly supported, you'll need that release or newer to run them.

• cagliostro-v3↗

  bench-labs

• omniasr-llm-300m-v2↗

  bezzam

• ostroumnici-bg-128m↗

  CaptainStefanov