Duplicate detection and removal (exact + near-duplicate)  ·  Quality scoring with flagging system  ·  Token length filtering (10–512 tokens)  ·  Malformed structure detection  ·  Automated quality reporting  ·  Dataset versioning with quality tracking

Validation-based early stopping  ·  Overfitting detection (threshold: 2.0)  ·  Best checkpoint selection via eval_loss  ·  Gradient checkpointing for memory efficiency  ·  MLflow experiment tracking with DagsHub  ·  Target modules: all attention + MLP projections

Automated dataset quality analysis  ·  Duplicate detection and removal  ·  Quality scoring and filtering  ·  Dataset versioning system  ·  Comprehensive quality reporting

# Load model from Hugging Face Hub
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Nasim435/Qwen-3B-Automotive-20K"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
 model_name,
 device_map="auto",
 torch_dtype="auto"
)
prompt = "Explain symptoms of a failing alternator and diagnostic steps."
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
 messages,
 tokenize=False,
 add_generation_prompt=True
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(
 **inputs,
 max_new_tokens=200,
 temperature=0.7,
 top_p=0.9,
 do_sample=True,
 pad_token_id=tokenizer.eos_token_id
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

• Research model — experimental fine-tune, not intended for production safety systems
• Hallucination risk — may generate inaccurate automotive advice (8.4/10 risk score)
• Safety critical — not suitable for safety-critical or professional mechanical decision-making
• Domain scope — trained on 20K samples; generalization beyond automotive may be limited
• Quality assurance — 96.6% data quality score with 263 flagged samples requiring review

Real-time GPU utilization tracking  ·  Memory usage profiling  ·  Training throughput monitoring  ·  Automated performance benchmarking