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AI 모델 84759

• Ling 3.1 Flash↗

  inclusionai

  Ling 3.1 Flash는 inclusionAI의 하이브리드 추론 혼합-전문가 모델로, 총 560B 파라미터 중 25B 개의 활성 파라미터를 갖추고 있습니다.

• nach0_base↗

  insilicomedicine

  Tasks Datasets used for training and evaluation. Colour represents the type of tasks. Yellow and blue datasets are single-domain, typically requiring regression/classification losses or generation in the target domain (natural language or SMILES strings). Gradients from yellow to blue represent cross-domain generation tasks that require natural language input and SMILES output, or vise versa. The simplest way to use the large version of the model is to run the megatron_t5_seq2seq_eval.py or megatron_t5_seq2seq_finetune.py script. Prior to executing the script, prepare the input (prompts) and output (responses) files and set up the config file. The input file should have prompts on each line, which need to be preprocessed using the `add_special_symbols` function mentioned above.

• japanese-sentiment-analysis↗

  jarvisx17

• Qwen3.5-35B-A3B-DASHQ-Q2-GGUF↗

  jkim96

• spicy-realism-nsfw-mix-v30-sdxl↗

  John6666

• kling-lip-sync↗

  快手 · 可灵

  리핑입니다. 텍스트 또는 이미지로 생성된 영상 결과물에 맞춰 리핑 영상을 생성하는 기능입니다. 가격은 원본 영상의 길이에 따라 달라지며, 5초 영상은 1배, 10초 영상은 2배입니다.

• ROSE↗

  Kunbyte

• Qwen3.5-27B-ultra-uncensored-heretic-v2-GGUF↗

  llmfan46

• Qwen3-4B-Instruct-2507-MLX-4bit↗

  lmstudio-community

  _👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: Qwen **Original model**: Qwen3-4B-Instruct-2507 **MLX quantization**: provided by LM Studio team using mlx_lm 4-bit quantized version of Qwen3-4B-Instruct-2507 using MLX, optimized for Apple Silicon.

• Qwen3.8-Distill-35B-A3B-Coder-Abliterated↗

  Lord-H4D3ZS

  A 2-bit **ROCmFPX** GGUF of a distilled Qwen3.6-35B-A3B (MoE, 256 experts / ~3B active), sized to run on a **16GB consumer GPU**. Ships with the MTP (`nextn`) head and build instructions for the matching runtime. > **Honest status:** this is a proof-of-concept. On the internal 10-task smoke eval the distilled > model **tied its base** (6/10 vs 6/10) — no regression, no measurable gain yet — and it is now > quantized to 2-bit, which trades quality for fit. Publishing it as a reproducible artifact of the > pipeline (distill → graft MTP → ROCmFPX 2-bit GGUF), **not** as a benchmark-winning coder. > The quality fix is a larger, tool-calling-heavy corpus — a separate follow-up run. 16GB card: context and concurrency share one KV pool — pick single-stream long context (`-c 32768 -np 1`) **or** many short sessions (`-c 8192 -np 8`).