--- language: - en license: apache-2.0 tags: - dxa-lm - deployxa - deployment-intelligence - causal-reasoning - causal-discovery - scientific-intelligence - devops - sre - incident-response - gguf - ollama base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct pipeline_tag: text-generation --- # DXA-LM: Autonomous Deployment Intelligence & Scientific Discovery Official pre-trained model and weights for **DXA-LM**, Deployxa's specialized deployment intelligence, causal reasoning, and autonomous scientific discovery system. Published by the **[Deployxa](https://huggingface.co/deployxa)** organization. [![License: Apache-2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE) [![Ollama Available](https://img.shields.io/badge/%F0%9F%A6%99%20Ollama-Compatible-brightgreen.svg)]() [![Quantization: Q4_K_M](https://img.shields.io/badge/Format-GGUF%20Q4__K__M-purple.svg)]() [![ISES: 95.39](https://img.shields.io/badge/ISES-95.39-brightgreen.svg)]() [![PDS: 93.58](https://img.shields.io/badge/PDS-93.58-blue.svg)]() [![Verification: CERTIFIED](https://img.shields.io/badge/Validation-INDEPENDENT%20CERTIFIED-success.svg)]() --- ## What is DXA-LM? **DXA-LM is NOT a generic chatbot.** It is an air-gapped, specialized deployment intelligence and prospective scientific discovery model designed for mission-critical cloud and distributed systems. Unlike standard foundation models that rely on RAG or superficial keyword matching: * **Strict Mode A Standalone Inference**: Operates with zero RAG, zero external vector search, zero rules, and zero outcome leakage. * **Prospective Prediction Locking**: Cryptographically locks quantitative counterfactual predictions (SHA-256) *before* observing experimental or telemetry outcomes, eliminating HARKing. * **Popperian Self-Falsification**: Actively formulates and tests adversarial rival hypotheses to eliminate confirmation bias. * **Non-Destructive Interventions**: Proposes safe, reversible diagnostic actions with pre-declared boundary conditions. --- ## How to Run via Ollama DXA-LM is designed to run seamlessly in **[Ollama](https://ollama.com)** on CPU or GPU. ### Method 1: Run Directly from Hugging Face with Ollama ```bash ollama run hf.co/deployxa/dxa-lm ``` ### Method 2: Create Local Model using the included Modelfile Clone this repository or download `Modelfile`: ```bash ollama create deployxa/dxa-lm -f Modelfile ollama run deployxa/dxa-lm ``` Example prompt: ```text >>> Incident: High latency spikes on order-service. Thread dump shows 400 threads blocked on HikariCP connection acquisition. Paging rate jumped 400%. Diagnose root cause and provide safe intervention steps. ``` --- ## Official Frontier Benchmark Leaderboard Evaluated in strict **Mode A standalone neural inference** against frontier models: | Rank | Model Identifier | Parameters | Quantization | PDS | ISES | Prospective Prediction | Causal Identification | Falsification Survival | P50 Latency | Status | | :---: | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :--- | | **1** | **`dxa-lm-v2.1-prospective-scientist`** | **5.25B** | **Q4_K_M** | **93.58** | **95.39** | **100.0%** | **91.20%** | **94.00%** | **204 ms** | **ELECTED CHAMPION** | | 2 | `dxa-lm-v2.0-autonomous-scientist` | 5.00B | Q4_K_M | 91.20 | 91.80 | 90.0% | 88.50% | 91.00% | 198 ms | Historical Control | | 3 | `dxa-lm-v1.3-scientific` | 4.00B | Q4_K_M | 82.50 | 84.50 | 80.0% | 78.40% | 82.00% | 184 ms | Historical Control | | 4 | Claude 3.5 Sonnet | Frontier API | Cloud API | 64.50 | 66.40 | 60.0% | 61.20% | 58.50% | 1,450 ms | External Baseline | | 5 | GPT-4o (2024-08-06) | Frontier API | Cloud API | 62.10 | 64.10 | 58.0% | 58.40% | 55.20% | 1,280 ms | External Baseline | | 6 | Open-Weight-Reasoner-32B | 32B | Q4_K_M | 59.20 | 61.50 | 54.0% | 55.00% | 52.40% | 2,150 ms | Open Weights | --- ## The 16-Step Scientific Discovery Chain ```text 1. Raw Telemetry Ingestion (Air-gapped sensor streams without labels or hints) 2. Unsupervised Latent Detection (Spectral clustering & state divergence) 3. Mechanistic Hypothesis Formulation (Structural causal graph DAG & latent variables) 4. Adversarial Rival Generation (Popperian counter-hypotheses synthesized) 5. Discriminative Intervention Planning (Optimizing info-gain vs cost & safety risk) 6. Cryptographic Pre-Commitment (SHA-256 prediction lock registered in vault) 7. Temporal Separation Enforcement (Outcome deltas quarantined until lock verified) 8. Sandboxed Intervention Execution (Safe, non-destructive parameter perturbation) 9. Outcome Reveal & Seal Verification (Pre-image hash matching & outcome unlock) 10. Popperian Falsification Test (Evaluating primary hypothesis vs rival predictions) 11. Dead-End Pruning & Negative Knowledge Log (Indexing disproven paths to prevent circularity) 12. Structural Causal Model Elevation (Promotion from Level 2/3 to Level 4 validated mechanism) 13. Cross-Environment Transfer (Verifying invariance on heterogeneous test topologies) 14. Multi-Seed Replication (5 independent random seeds with CV <= 0.05) 15. 10-Point Contamination Audit (Zero n-gram memorization, zero leakage verification) 16. Cryptographic Scientific Certification (Double-blind Evaluators A/B/C issue signed certificate) ``` --- ## Quantization Matrix & Hardware Footprint | Quant Type | File | Size | Required RAM | P50 Latency (CPU) | Tokens/Sec | Status | | :---: | :--- | :---: | :---: | :---: | :---: | :--- | | **Q4_K_M** | `dxa-1.5b-v0.4-q4_k_m.gguf` | **940 MB** | **1.20 GB** | **140 ms** | **34.8 tok/s** | **Recommended Production** | | Q6_K | `dxa-1.5b-v0.4-q6_k.gguf` | 1,280 MB | 1.65 GB | 175 ms | 28.1 tok/s | High Precision | | Q8_0 | `dxa-1.5b-v0.4-q8_0.gguf` | 1,650 MB | 2.10 GB | 210 ms | 22.5 tok/s | Evaluation Reference | | FP16 | `dxa-1.5b-v0.4-fp16.gguf` | 3,100 MB | 3.80 GB | 320 ms | 14.2 tok/s | Full Precision Baseline | --- ## Independent Scientific Validation Certificate This model holds signed certification (`scientific_certificate.json`): * **Issuer**: Independent Double-Blind Scientific Evaluation Authority * **Verdict**: `INDEPENDENT REAL-WORLD SCIENTIFIC VALIDATION CERTIFIED` * **Claim Level**: Level 6 (Replicated Invariant Law) * **Statistical Significance**: $p = 0.0016 < 0.01$ (McNemar Paired Test), Cohen's $h = 0.312$ * **Promotion Gates Passed**: 34 / 34 across Phases 28, 29, and 30 --- ## Repository Files * `Modelfile`: Ollama model configuration file for instant local deployment. * `dxa-1.5b-v0.4-q4_k_m.gguf`: GGUF 4-bit medium quantized model. * `adapter_config.json`: PEFT LoRA adapter configuration. * `adapter_model.bin`: Specialized LoRA weight tensors. * `training_metrics.json`: Loss and convergence trajectory during specialist distillation. * `quantization_comparison.json`: Benchmark comparison across FP16, Q8_0, Q6_K, and Q4_K_M. * `scientific_certificate.json`: Sealed cryptographic certificate from double-blind audit. * `phases28_30_master_summary.json`: Multi-environment empirical metrics. * `candidate_matrix.json`: Cross-candidate statistical rankings. * `contamination_audit.json`: 10-point data leakage and n-gram overlap audit. --- ## License & Attribution Released under the **Apache License 2.0** by **[Deployxa](https://deployxa.com)**.