07. Hugging Face TRL (Transformer Reinforcement Learning) & SFTTrainer Architecture • Model Fine-Tuning, Quantization & RLHF (PEFT, LoRA, vLLM) • Rishabh Ki Kaksha

Model Fine-Tuning, Quantization & RLHF (PEFT, LoRA, vLLM) › 07. Hugging Face TRL (Transformer Reinforcement Learning) & SFTTrainer Architecture

50 mins

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Course Syllabus

Module 1: Pre-training vs Fine-Tuning & Dataset Engineering

01. LLM Adaptation Paradigms: Pretraining, Continued Pretraining, Full Fine-Tuning vs PEFT 02. Fine-Tuning Dataset Engineering: Instruction Datasets (Alpaca/ShareGPT formats), Quality Filtering & Deduplication 03. Tokenization Traps: Prompt Formatting, Chat Templates (ChatML / Llama-3 format) & Loss Masking on Prompts

Module 2: Parameter-Efficient Fine-Tuning (PEFT) & LoRA / QLoRA Internals

04. Low-Rank Adaptation (LoRA) Mathematics: Intrinsic Rank Hypothesis, Matrix Factorization (W + B*A) & Alpha Scaling 05. QLoRA Deep Dive: 4-Bit NormalFloat (NF4), Double Quantization & Paged Optimizers for VRAM Efficiency 06. Target Modules Selection (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj) & Rank Tradeoffs

Module 3: Supervised Fine-Tuning (SFT) with HuggingFace TRL & Unsloth

07. Hugging Face TRL (Transformer Reinforcement Learning) & SFTTrainer Architecture 08. Ultra-Fast Fine-Tuning with Unsloth (Manual Triton Kernels & 5x Speedup on Single GPUs) 09. Monitoring Loss Curves: Overfitting Detection, Validation Perplexity & Catastrophic Forgetting Mitigation

Module 4: Alignment Algorithms: DPO (Direct Preference Optimization) & RLHF

10. RLHF (Reinforcement Learning from Human Feedback) Architecture: Reward Models & PPO (Proximal Policy Optimization) 11. Direct Preference Optimization (DPO): Eliminating Reward Models via Implicit Policy Optimization Math 12. ORPO (Odds Ratio Preference Optimization) & KTO (Kahneman-Tversky Optimization) Alignment Techniques

Module 5: Model Quantization (GGUF, AWQ, GPTQ) & High-Throughput vLLM Serving

13. Post-Training Quantization (PTQ): 4-Bit & 8-Bit Quantization Mathematics (Zero Point & Scale Factors) 14. Quantization Formats: GGUF (llama.cpp on CPU/Mac), AWQ (Activation-aware Weight Quantization) & GPTQ 15. Merging LoRA Adapters back into Base Model Weights for Zero-Inference Latency Overhead 16. High-Throughput Enterprise Model Serving with vLLM (PagedAttention, Continuous Batching & Tensor Parallelism)

Interactive Lesson Studio

# 07. Hugging Face TRL (Transformer Reinforcement Learning) & SFTTrainer Architecture

> Model Fine-Tuning & Quantization Notes by Rishabh - Topic 07

---

### 1. `SFTTrainer` (Supervised Fine-Tuning Engine) 🚀

Hugging Face TRL library ka specialized trainer jo causal language modeling, packing, aur PEFT LoRA integration ko single automated loop me bind karta hai:

```python
from trl import SFTTrainer, SFTConfig
from peft import LoraConfig

peft_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

trainer = SFTTrainer(
    model=model,
    train_dataset=dataset,
    peft_config=peft_config,
    dataset_text_field="text",
    max_seq_length=2048,
    packing=True, # Packs multiple short samples into single 2048 context (3x Speedup!)
    args=SFTConfig(
        output_dir="./lora_checkpoints",
        learning_rate=2e-4,
        lr_scheduler_type="cosine",
        per_device_train_batch_size=4,
        gradient_accumulation_steps=4,
        warmup_ratio=0.03,
        logging_steps=10,
        bf16=True,
    ),
)
trainer.train()
```

---

### 2. Sample Packing (Constant GPU Utilization ⚡)

Variable length sentences pad karne par GPU 70% time `<PAD>` tokens multiply karne me waste karta hai.  
**Packing:** Multiple short conversations ko `<|endoftext|>` token se separate karke single contiguous 2048 context block me pack karta hai!

---

### 3. Interview Ke 3 Sawal Is Topic Se

1. **Gradient Accumulation Steps effective batch size kaise banata hai?** -> $\text{Effective Batch} = \text{Per Device Batch} \times \text{Grad Accum Steps} \times \text{Num GPUs}$ (Allows simulating batch size 64 on single 24GB GPU).
2. **DataCollatorForCompletionOnlyLM prompt tokens par loss mask kaise karta hai?** -> Response token `### Response:` ke baad wale tokens par hi cross-entropy loss compute karta hai jisse model user questions memorise na kare.
3. **Cosine Annealing Learning Rate schedule kyu use hota hai?** -> Learning rate ko smoothly cosine curve me decay karke local minima me fine settling achieve karna.

---
**Next Topic:** 08. Ultra-Fast Fine-Tuning with Unsloth (Manual Triton Kernels & 5x Speedup on Single GPUs)

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