upd3
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README.md
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@@ -76,16 +76,16 @@ When used with the provided `Modelfile`, the model outputs **exactly one JSON ob
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### Schema
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{
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"verdict": "true | false | uncertain",
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"reason": "string",
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"confidence": 0.0,
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"evidence": ["string"],
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"assumptions": ["string"],
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"next_actions": ["string"]
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}
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Rules
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confidence is a heuristic value between 0.0 and 1.0
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If information is missing, the verdict must be uncertain
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Stop behavior is enforced by the Modelfile
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How to run with Ollama
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Create the model locally:
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bash
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Copy code
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ollama create logic-reasoner-v2 -f Modelfile
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Example request:
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"model": "logic-reasoner-v2",
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"stream": false,
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"prompt": "Input: DCGM exporter reports 0 GPUs across all nodes. Question: Is the system healthy?"
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}'
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Quantization
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Format: GGUF
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Quantization: Q4_K_M
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Optimized for low-latency operational inference
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Provenance
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This model was built and packaged as part of the LLM FUN project on NVIDIA DGX B200 infrastructure using:
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Kubernetes (RKE2)
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Ollama
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OpenWebUI
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The Modelfile is a core part of the model behavior and must be used to reproduce the intended output guarantees.
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### Schema
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{
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"verdict": "true | false | uncertain",
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"reason": "string",
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"confidence": 0.0,
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"evidence": ["string"],
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"assumptions": ["string"],
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"next_actions": ["string"]
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}
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## Rules
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confidence is a heuristic value between 0.0 and 1.0
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If information is missing, the verdict must be uncertain
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Stop behavior is enforced by the Modelfile
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## How to run with Ollama
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Create the model locally:
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ollama create logic-reasoner-v2 -f Modelfile
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## Example request:
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curl http://localhost:11434/api/generate -d '{
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"model": "logic-reasoner-v2",
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"stream": false,
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"prompt": "Input: DCGM exporter reports 0 GPUs across all nodes. Question: Is the system healthy?"
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}'
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## Quantization
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Format: GGUF
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Quantization: Q4_K_M
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Optimized for low-latency operational inference
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## Provenance
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This model was built and packaged as part of the LLM FUN project on NVIDIA DGX B200 infrastructure using:
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+
Kubernetes (RKE2)
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| 121 |
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| 122 |
+
Ollama
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+
OpenWebUI
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The Modelfile is a core part of the model behavior and must be used to reproduce the intended output guarantees.
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