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AI モデル 84759

• Ling 3.1 Flash↗

  inclusionai

  Ling 3.1 FlashはinclusionAIによるハイブリッド推論の混合専門家モデルで、総パラメータ560億のうちアクティブなパラメータが250億あります。

• 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

  对口型。是对文生/图生视频结果生成对口型视频；价格与原视频时长有关，5s视频1倍，10s 视频 2 倍

• 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`).