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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.
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---
## 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)**.
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