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README.md

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+---
+language:
+- en
+license: mit
+tags:
+- serverlex
+- code-generation
+- diagnostics
+- machine-learning
+- tabular
+- lora
+- auto-copilot
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Serverlex Copilot 0.5B ⚡
+
+**Serverlex Copilot 0.5B** is an offline, specialized AI diagnostic agent fine-tuned exclusively for [Serverlex](https://github.com/Uwais-ML/Serverlex)—the production-safe machine learning preprocessing, diagnostics, and serving platform.
+
+At just **0.5 Billion parameters (~980MB in fp16, ~340MB in 4-bit)**, this model is lightweight enough to run locally on standard CPUs, Apple Silicon Macs, or edge environments with **zero external API calls or GPU requirements**.
+
+---
+
+## 📌 Capabilities
+
+1. **Pre-Flight Data Quality Audit Triage**:
+   - Analyzes `DataGuard` logs and audit reports.
+   - Detects and explains ID memorization hazards, PII privacy concerns, and infinite value defects.
+2. **Mathematical Target Leakage Mitigation**:
+   - Identifies suspicious proxy features ($|r| \ge 0.90$) and writes exact code to drop or remediate them.
+3. **Production Drift Interpretation**:
+   - Interprets `DriftDetector` Population Stability Index (PSI) and Kolmogorov-Smirnov (KS) statistics.
+   - Prescribes retraining strategies and automated webhook alerting configurations.
+4. **Hardened Serving & Cryptographic Verification**:
+   - Diagnoses HMAC-SHA256 signature verification mismatches and tamper attempts in `ModelDeployer`.
+   - Explains strict schema validation errors (unseen categories, missing features, invalid types).
+5. **End-to-End Workflow Synthesis**:
+   - Generates production-ready, leakage-free Serverlex code (cleaners, pipeline encoders, nested CV tuners, and containerized FastAPI exports).
+
+---
+
+## 🚀 Quickstart & Inference
+
+### 1. Directly via Serverlex (Recommended)
+
+```python
+import serverlex
+
+# Enable real-time automatic exception interception & guidance
+serverlex.enable_auto_copilot()
+
+# Or use the standalone Copilot agent
+from serverlex import ServerlexCopilot
+
+copilot = ServerlexCopilot()
+log = "2026-11-06 10:39:12 [ERROR] DataGuard: Suspicious target leakage: feature post_op_charges has correlation 0.9842 with target total_hospital_cost"
+
+print(copilot.diagnose_log(log))
+```
+
+### 2. Via Hugging Face `transformers`
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+MODEL_ID = "Uwais-ML-dev/serverlex-copilot-0.5b"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+prompt = """### Instruction:
+Analyze this Serverlex log and output the fix.
+
+### Input:
+2026-11-06 10:39:12 [ERROR] DataGuard: Suspicious target leakage: feature 'hospital_charges' has correlation 0.9842 with target 'total_hospital_cost'
+
+### Response:
+"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=300, do_sample=False)
+print(tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True))
+```
+
+---
+
+## 🏋️ Training Details
+
+- **Base Architecture**: `Qwen/Qwen2.5-0.5B-Instruct`
+- **Methodology**: LoRA (Low-Rank Adaptation) merged into standalone fp16 base weights
+  - Rank ($r$): 16
+  - Alpha ($\alpha$): 32
+  - Target Modules: `q_proj`, `k_proj`, `v_proj`, `o_proj`
+- **Dataset**: 5,150 curated instructional pairs covering real Serverlex logs, multi-step recipes, and diagnostics.
+- **Hardware**: Fine-tuned on a single NVIDIA T4 GPU in Google Colab.
+- **Final Training Loss**: 0.1472
+
+---
+
+## 📄 License & Attribution
+
+Released under the **MIT License**. Created by the Serverlex Team as part of the **Serverlex 2.0 Platform**.