Instructions to use Uwais-ML-dev/serverlex-copilot-0.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Uwais-ML-dev/serverlex-copilot-0.5b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Uwais-ML-dev/serverlex-copilot-0.5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Uwais-ML-dev/serverlex-copilot-0.5b")
model = AutoModelForCausalLM.from_pretrained("Uwais-ML-dev/serverlex-copilot-0.5b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Uwais-ML-dev/serverlex-copilot-0.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Uwais-ML-dev/serverlex-copilot-0.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Uwais-ML-dev/serverlex-copilot-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Uwais-ML-dev/serverlex-copilot-0.5b

SGLang

How to use Uwais-ML-dev/serverlex-copilot-0.5b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Uwais-ML-dev/serverlex-copilot-0.5b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Uwais-ML-dev/serverlex-copilot-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Uwais-ML-dev/serverlex-copilot-0.5b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Uwais-ML-dev/serverlex-copilot-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Uwais-ML-dev/serverlex-copilot-0.5b with Docker Model Runner:
```
docker model run hf.co/Uwais-ML-dev/serverlex-copilot-0.5b
```

serverlex-copilot-0.5b / README.md

Uwais-ML-dev

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metadata

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—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

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.
Mathematical Target Leakage Mitigation:
- Identifies suspicious proxy features ($|r| \ge 0.90$) and writes exact code to drop or remediate them.
Production Drift Interpretation:
- Interprets DriftDetector Population Stability Index (PSI) and Kolmogorov-Smirnov (KS) statistics.
- Prescribes retraining strategies and automated webhook alerting configurations.
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).
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)

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`

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.