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TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
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Poseidon-L

camlab-ethz

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Poseidon-T

camlab-ethz

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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INT4 weight-only quantization of zai-org/GLM-5.3-Flash with the BF16 MTP draft head kept for speculative decoding. Only the 36,288 routed-expert GEMMs are INT4 (GPTQ, symmetric, group-size 128); attention, router, shared experts, embeddings, the vision tower and the MTP head stay in BF16. Not a new model — all capability comes from the base model. > **Two things that bite.** If your `config.json` predates 2026-09-08, re-download it — older copies fail in vLLM with `KeyError: 'layers.0.mlp.gate_up_proj.weight'` (weights are unchanged). And always pass `--max-num-seqs ≤ 512` — the vLLM default of 1024 exceeds this hybrid linear-attention model's 512 Mamba/KDA-state cache blocks. **H100 / H200, TP=4 (pinned image)** — the Quick start command is the benchmarked recipe. `num_speculative_tokens: 2` is the sweet spot on Hopper (52–55% acceptance; N=5 collapses acceptance to ~30%). Keep prefix caching off — it measured −2…−5% on H200. On 8× H200, two independent TP=4 replicas behind a load balancer beat one TP=8 endpoint by +28–38% aggregate at c128–c512; TP=8 wins single-stream and holds one 7.8M-token pool. On 2× H200 (89.5 GiB weights per GPU) add `--tensor-parallel-size 2 --max-num-seqs 128 --max-cudagraph-capture-size 128` for 262K, or `--max-model-len 1048576 --max-num-seqs 16 --gpu-memory-utilization 0.95 --max-cudagraph-capture-size 64 --max-num-batched-tokens 4096` for 1M.

Visual QA
Input Text · Image→ Output Text
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ema-lightning

canberkkkkkk

**Tiny, fast and accurate Turkish text to speech.** A 5.6M-parameter DiT acoustic model and a 3M-parameter vocoder: 8.6M in total, about 34 MB. Apache 2.0. - **Most accurate on Freya-TR-Eval:** 0.92% WER, the lowest of every system measured, including ElevenLabs v4, Gemini 3.8 and the 2.38B-parameter Trendyol-TTS. - **Fast:** first audio in 3.86 ms and 440× faster than real time on an RTX 4090; 1,316× with batching. - **Streams:** audio starts while the rest of the text is still being generated. - **Cheap:** about $0.0085 per million characters on a rented RTX 4090. - **Runs anywhere:** GPU or CPU, fully offline. No API key, and no text or audio leaves the machine. - **One model serves many callers:** call it from as many threads as you like; its scheduler, Playhead, runs everyone's work together on the GPU. - **Code:** [EMA Lightning on GitHub][github] - **Package:** `pip install ema-lightning` - **Text frontend:** normalizer-tr, by Erdem Tuna

Speech synthesis
Input Text→ Output Audio
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