Ornith1.5-Genesis-Hermes

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What Ornith1.5-Genesis-Hermes is

🌟 Ornith-1.5-35B-A3B - Genesis Hermes An experimental model processed via an algorithm and created upon request from China. ⚡ https://web.tribute.tg/d/KIH ⚡ If you like this Genesis LLM release you can donate to me via @Tribute bot in Telegram messenger and support future Genesis LLM development. ⚡ Why Genesis project exists? During training, ALL models don't just learn knowledge - they also accumulate random noise in their tensors. This noise builds up and creates something I call the Noise Gate - a fundamental barrier that stops LLM models from learning further and makes them unstable, verbose, and prone to hallucinations. My approach reduces this noise. It repairs the signal without touching the learned knowledge and gradient. The result is a model that consistent in performance, context clarity and following instructions, because it's no longer fighting its own internal chaos. What is Genesis? Genesis is post training data regeneration and calibrarion algorythm for neural networks (LLM) in GGUF format that I made with AI help during almost half a year of development. It's optimized, architecture independent, works with any model in GGUF format and based on mathematical statistics. I don't train or finetune models, I repair purity of signal in them instead on Google Collab Free on Tesla T4 GPU via Python based on how models learns information. On first stage I scan ssmconv1d tensors in model, they handle long context memory. I repair balance between heads in them. On second stage I scan model and detect noise in tensors via custom SVD. During scanning I exclude tokenembd.weight, output.weight, ffngateinpshexp.weight, 1D tensors, bias and norms. Then I reduce training noise in tensors via custom SVD with preserved training data, 99% of siginal and learned gradient. On third stage, I scan blocks in model via chunks via 3 parameters and pick best one that fits to weight distribution in tensor. Best picked chunk replaces zero chunks in broken tensor without touching learned structure in model Model is based on Ornith-1.5-35B-A3B-GGUF base. And DJLougen/hermes-qwen3.5-35b-a3b-GGUF finetune for Hermes agent. I transferred data from finetune on Hermes dataset (aroun

Summarised from the published model card. Read the full card on the HuggingFace links below.

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Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

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Call it like any OpenAI endpoint

Once AxForge deploys ornith1-5-genesis-hermes for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (ornith1-5-genesis-hermes below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"ornith1-5-genesis-hermes","messages":[{"role":"user","content":"Hello"}]}'

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