If you want the fastest local installation for this model, use standard pip packages.
Check out the detailed setup guide below to begin.
1-click setup: the app automatically fetches the large weight files.
There is no manual tuning required; the builder deploys the best matching configuration.
SHA sum: 56e6ad2a10d13d9c448e70f9fd403830 | Updated: 2026-06-26
The model Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-NEO-CODE-Di-IMatrix-MAX-GGUF is a massive 40‑billion parameter language model designed for high‑performance inference. It leverages an advanced Transformer‑based architecture with multi‑head attention and a novel Di‑IMatrix optimization layer that dramatically reduces memory footprint while preserving accuracy. The model has been trained on a diverse, web‑scale corpus, enabling it to generate coherent, context‑aware responses across technical, creative, and conversational domains. Benchmarks show that it outperforms many existing open‑source models in reasoning, coding, and language understanding tasks, thanks to its Opus‑Deckard fine‑tuning pipeline. Its uncensored thinking mode encourages transparent reasoning steps, making it especially valuable for research and educational applications.
| Specification | Value |
|---|---|
| Parameters | 40 B |
| Context Length | 8 K tokens |
| Training Data | ≈1.5 trillion tokens |
| Inference Speed | ≈200 tokens/s (GPU) |
| Quantization | GGUF (Q4_K_M) |
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File Hash: 3d882d5f7445a210ee0650f51b49db1d — Last update: 2026-06-30|
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File hash: d3244d056318529aae7dfc6c870cdfe3 (Update date: 2026-06-29)|
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To install this model locally in the shortest time, opt for a direct curl execution.
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The setup auto-downloads all needed files (several GBs).
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Hash-sum → 
Updated on 2026-06-24The Kimi-K2.5-NVFP4 model introduces a breakthrough in efficient inference for large language tasks. Built on a sparse-attention architecture, it reduces computational load while preserving high contextual understanding. The model achieves state‑of‑the‑art performance on benchmarks such as MMLU and TriviaQA, often outperforming larger parameter counterparts. Its parameter count and memory footprint are optimized for deployment on consumer‑grade hardware, as illustrated in the comparison table below.
| Training Data Size | 1.5 TB |
|---|---|
| Parameter Count | 7B |
| Inference Latency (ms) | 12 |
| GPU Memory (GB) | 16 |
The following table provides key metrics including training data size, inference latency, and GPU memory usage, enabling developers to assess suitability for their applications.
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Hash code: 83402f7d7a9265ecfd1a198ccfc36108 — Last modification: 2026-06-28|
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File Hash: d885c7603e46cdfb1f4396d7ace33a33 — Last update: 2026-06-24|
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Deploying this model locally is quickest when done via a simple curl command.
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An automated background process downloads all required large-scale files.
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Hash Check: 3545d7fde31cc15221f5126ca9672afb | Sulphur-2-base is a next‑generation language model designed to excel in scientific reasoning and code generation. It leverages an enhanced transformer architecture with a 2‑trillion‑parameter base, enabling unprecedented contextual depth. The model incorporates specialized fine‑tuning for chemistry and physics domains, delivering high‑fidelity predictions with reduced hallucinations. Performance benchmarks show a 15% improvement over prior Sulphur variants in multi‑step problem solving. Below is a quick comparison of key specifications against its nearest competitor:
| Metric | Sulphur-2-base | Competitor X |
|---|---|---|
| Parameters | 2 trillion | 1.5 trillion |
| Domain Accuracy | 92% | 84% |
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Hash Value: 94e658b5b39cfa755edcca98f19d64ee | 
Update: 2026-06-27|
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Release Hash: e60e8cede0b47e7cc180aab3f62f5836 • Date: 2026-06-23|
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