Building and Deploying a Fine-Tuned LLM for Domain-Specific Q&A with LoRA
In 2025, domain-specific AI assistants have become essential tools for enterprises, but training large language models from scratch remains prohibitively expensive. Enter LoRA (Low-Rank Adaptation) - a revolutionary fine-tuning technique that enables organizations to create highly specialized Q&A systems at a fraction of the cost. This comprehensive guide explores how to build and deploy production-ready domain-specific LLMs using LoRA, covering everything from data preparation and model selection to deployment optimization and monitoring. Whether you're building a medical diagnosis assistant, legal research tool, or technical support chatbot, mastering LoRA fine-tuning will transform how you leverage AI for specialized knowledge domains.
🚀 Why LoRA Dominates Domain-Specific AI in 2025
LoRA has emerged as the gold standard for efficient model fine-tuning, offering dramatic reductions in computational requirements while maintaining or even improving performance on specialized tasks. Here's why it's become indispensable for domain-specific AI:
- 95% Parameter Efficiency: Train only 1-5% of model parameters instead of full fine-tuning
- Rapid Iteration: Experiment with different domains and datasets in hours, not days
- Cost Optimization: Reduce training costs from thousands to hundreds of dollars
- Model Portability: Small LoRA adapters can be shared and combined easily
- Multi-Domain Flexibility: Switch between different domain experts with adapter swapping
🔧 Understanding LoRA: The Technical Foundation
LoRA works by injecting trainable rank decomposition matrices into transformer layers, focusing adaptation on the attention mechanisms where most domain knowledge is captured. This approach preserves the original model's general capabilities while adding specialized domain expertise.
- Rank Decomposition: Represents weight updates as low-rank matrices A and B
- Attention Adaptation: Focuses on query, key, value, and output projections
- Mergeable Weights: Adapters can be merged for inference efficiency
- Hyperparameter Optimization: Rank, alpha, and dropout control adaptation strength
- Multi-Adapter Architecture: Support for loading multiple domain adapters simultaneously
💻 Complete LoRA Fine-Tuning Implementation
Here's a complete implementation for fine-tuning a Llama 3 model for medical Q&A using LoRA with the Hugging Face ecosystem:
# lora_fine_tuning.py - Complete Medical Q&A Fine-tuning
import torch
from transformers import (
AutoTokenizer, AutoModelForCausalLM,
TrainingArguments, DataCollatorForSeq2Seq,
BitsAndBytesConfig
)
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from trl import SFTTrainer
from datasets import load_dataset
import wandb
# Configuration
MODEL_NAME = "meta-llama/Meta-Llama-3-8B-Instruct"
DATASET_PATH = "medical_qa_dataset"
OUTPUT_DIR = "./medical-llama-lora"
LORA_RANK = 16
LORA_ALPHA = 32
LORA_DROPOUT = 0.1
# Quantization config for memory efficiency
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
quantization_config=bnb_config,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16
)
# Prepare model for PEFT training
model = prepare_model_for_kbit_training(model)
# LoRA configuration
lora_config = LoraConfig(
r=LORA_RANK,
lora_alpha=LORA_ALPHA,
lora_dropout=LORA_DROPOUT,
bias="none",
task_type="CAUSAL_LM",
target_modules=[
"q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj"
]
)
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
# Load and preprocess medical Q&A dataset
def load_medical_dataset():
dataset = load_dataset(DATASET_PATH)
def format_instruction(sample):
return f"""### Instruction:
You are a medical expert. Answer the following question based on medical knowledge.
### Question:
{sample['question']}
### Context:
{sample['context']}
### Response:
{sample['answer']}"""
def tokenize_function(examples):
texts = [format_instruction(ex) for ex in examples]
tokenized = tokenizer(
texts,
truncation=True,
padding=False,
max_length=2048,
return_tensors=None
)
tokenized["labels"] = tokenized["input_ids"].copy()
return tokenized
tokenized_dataset = dataset.map(
tokenize_function,
batched=True,
remove_columns=dataset["train"].column_names
)
return tokenized_dataset
dataset = load_medical_dataset()
# Training arguments
training_args = TrainingArguments(
output_dir=OUTPUT_DIR,
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
gradient_accumulation_steps=4,
learning_rate=2e-4,
num_train_epochs=3,
logging_steps=50,
save_steps=500,
eval_steps=500,
evaluation_strategy="steps",
save_strategy="steps",
load_best_model_at_end=True,
metric_for_best_model="eval_loss",
greater_is_better=False,
warmup_steps=100,
lr_scheduler_type="cosine",
optim="paged_adamw_8bit",
fp16=False,
bf16=True,
max_grad_norm=0.3,
report_to="wandb",
run_name="medical-llama-lora"
)
# Create trainer
trainer = SFTTrainer(
model=model,
args=training_args,
train_dataset=dataset["train"],
eval_dataset=dataset["validation"],
dataset_text_field="text",
max_seq_length=2048,
tokenizer=tokenizer,
packing=True,
data_collator=DataCollatorForSeq2Seq(
tokenizer,
pad_to_multiple_of=8,
return_tensors="pt",
padding=True
)
)
# Start training
print("Starting LoRA fine-tuning...")
trainer.train()
# Save the fine-tuned adapter
trainer.save_model()
tokenizer.save_pretrained(OUTPUT_DIR)
print("Training completed successfully!")
📊 Advanced Data Preparation & Augmentation
High-quality domain-specific data is crucial for effective fine-tuning. Here's how to create and augment specialized Q&A datasets:
# data_preparation.py - Advanced Dataset Creation
import json
import pandas as pd
from datasets import Dataset, concatenate_datasets
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
class DomainDataPreparer:
def __init__(self, domain_name):
self.domain_name = domain_name
self.similarity_model = SentenceTransformer('all-MiniLM-L6-v2')
def load_and_clean_documents(self, document_paths):
"""Load domain documents and clean for training"""
documents = []
for path in document_paths:
with open(path, 'r', encoding='utf-8') as f:
content = f.read()
# Split into chunks with overlap
chunks = self._chunk_document(content, chunk_size=512, overlap=50)
documents.extend(chunks)
return documents
def generate_qa_pairs(self, documents, num_questions_per_chunk=3):
"""Generate Q&A pairs from documents using LLM"""
from openai import OpenAI
client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
qa_pairs = []
for doc in documents:
prompt = f"""Generate {num_questions_per_chunk} question-answer pairs based on the following text.
Focus on key concepts, definitions, and important details.
Text: {doc}
Format as JSON:
{{
"questions": [
{{
"question": "question text",
"answer": "answer text",
"context": "relevant context from text"
}}
]
}}"""
try:
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.7
)
result = json.loads(response.choices[0].message.content)
qa_pairs.extend(result["questions"])
except Exception as e:
print(f"Error generating Q&A: {e}")
continue
return qa_pairs
def augment_dataset(self, qa_pairs, augmentation_factor=2):
"""Augment dataset with paraphrasing and difficulty variations"""
augmented_pairs = []
for pair in qa_pairs:
# Original pair
augmented_pairs.append(pair)
# Paraphrase questions
paraphrased = self._paraphrase_question(pair["question"])
if paraphrased and paraphrased != pair["question"]:
augmented_pairs.append({
"question": paraphrased,
"answer": pair["answer"],
"context": pair["context"]
})
# Create multiple choice variations
mc_variants = self._create_multiple_choice(pair)
augmented_pairs.extend(mc_variants)
return augmented_pairs
def create_final_dataset(self, qa_pairs, train_ratio=0.8):
"""Create train/validation splits with quality filtering"""
df = pd.DataFrame(qa_pairs)
# Filter low-quality pairs
df = self._filter_low_quality(df)
# Remove duplicates
df = self._remove_similar_questions(df)
# Split dataset
train_size = int(len(df) * train_ratio)
train_df = df[:train_size]
val_df = df[train_size:]
train_dataset = Dataset.from_pandas(train_df)
val_dataset = Dataset.from_pandas(val_df)
return {
"train": train_dataset,
"validation": val_dataset
}
def _chunk_document(self, text, chunk_size=512, overlap=50):
"""Split document into overlapping chunks"""
words = text.split()
chunks = []
for i in range(0, len(words), chunk_size - overlap):
chunk = ' '.join(words[i:i + chunk_size])
chunks.append(chunk)
return chunks
def _paraphrase_question(self, question):
"""Paraphrase question using rule-based and model-based approaches"""
# Simple rule-based paraphrasing
paraphrases = [
question,
f"Can you explain: {question}",
f"What is meant by: {question}",
f"Could you elaborate on: {question}"
]
# Use embedding similarity to choose best paraphrase
embeddings = self.similarity_model.encode(paraphrases)
original_embedding = self.similarity_model.encode([question])
similarities = cosine_similarity([original_embedding[0]], embeddings)[0]
best_idx = np.argmax(similarities[1:]) + 1 # Skip original
return paraphrases[best_idx]
def _create_multiple_choice(self, qa_pair):
"""Create multiple choice variations"""
# Implementation for generating distractors
variants = []
# ... multiple choice generation logic
return variants
def _filter_low_quality(self, df):
"""Filter out low-quality Q&A pairs"""
# Remove very short questions/answers
df = df[df['question'].str.len() > 10]
df = df[df['answer'].str.len() > 20]
# Remove questions that are too similar to answers
df['q_a_similarity'] = df.apply(
lambda x: cosine_similarity(
self.similarity_model.encode([x['question']]),
self.similarity_model.encode([x['answer']])
)[0][0],
axis=1
)
df = df[df['q_a_similarity'] < 0.8]
return df
def _remove_similar_questions(self, df, similarity_threshold=0.9):
"""Remove semantically similar questions"""
if len(df) == 0:
return df
question_embeddings = self.similarity_model.encode(df['question'].tolist())
similarity_matrix = cosine_similarity(question_embeddings)
to_remove = set()
for i in range(len(similarity_matrix)):
if i in to_remove:
continue
for j in range(i + 1, len(similarity_matrix)):
if similarity_matrix[i][j] > similarity_threshold:
to_remove.add(j)
return df[~df.index.isin(to_remove)]
# Usage example
preparer = DomainDataPreparer("medical")
documents = preparer.load_and_clean_documents(["medical_textbook.pdf"])
qa_pairs = preparer.generate_qa_pairs(documents)
augmented_pairs = preparer.augment_dataset(qa_pairs)
final_dataset = preparer.create_final_dataset(augmented_pairs)
🚀 Production Deployment with FastAPI & vLLM
Deploying fine-tuned models requires efficient inference and robust API design. Here's a production-ready deployment setup:
# app.py - Production FastAPI Deployment
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from contextlib import asynccontextmanager
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
from vllm import LLM, SamplingParams
import logging
from prometheus_fastapi_instrumentator import Instrumentator
import os
# Configuration
MODEL_BASE = "meta-llama/Meta-Llama-3-8B-Instruct"
LORA_ADAPTER_PATH = "./medical-llama-lora"
MODEL_CACHE_DIR = "./model_cache"
# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class QnARequest(BaseModel):
question: str
context: str = ""
max_length: int = 1024
temperature: float = 0.7
top_p: float = 0.9
class QnAResponse(BaseModel):
answer: str
confidence: float
processing_time: float
tokens_generated: int
# Global model instances
llm = None
tokenizer = None
@asynccontextmanager
async def lifespan(app: FastAPI):
# Startup: Load models
global llm, tokenizer
try:
logger.info("Loading base model and tokenizer...")
# Load with vLLM for optimized inference
llm = LLM(
model=MODEL_BASE,
tensor_parallel_size=torch.cuda.device_count(),
gpu_memory_utilization=0.9,
max_model_len=4096,
enable_prefix_caching=True,
trust_remote_code=True
)
# Load LoRA adapter
logger.info("Loading LoRA adapter...")
base_model = AutoModelForCausalLM.from_pretrained(
MODEL_BASE,
torch_dtype=torch.bfloat16,
device_map="auto",
cache_dir=MODEL_CACHE_DIR
)
model = PeftModel.from_pretrained(
base_model,
LORA_ADAPTER_PATH,
torch_dtype=torch.bfloat16
)
# Merge LoRA weights for efficient inference
model = model.merge_and_unload()
tokenizer = AutoTokenizer.from_pretrained(MODEL_BASE)
tokenizer.pad_token = tokenizer.eos_token
logger.info("Models loaded successfully")
except Exception as e:
logger.error(f"Error loading models: {e}")
raise
yield
# Shutdown: Cleanup
if llm:
del llm
torch.cuda.empty_cache()
app = FastAPI(
title="Domain-Specific Q&A API",
description="API for medical domain question answering",
version="1.0.0",
lifespan=lifespan
)
# Add metrics endpoint
Instrumentator().instrument(app).expose(app)
def format_prompt(question: str, context: str = "") -> str:
"""Format the prompt for domain-specific Q&A"""
if context:
prompt = f"""### Instruction:
You are a medical expert. Answer the question based on the provided context and your medical knowledge.
### Context:
{context}
### Question:
{question}
### Response:"""
else:
prompt = f"""### Instruction:
You are a medical expert. Answer the following question based on your medical knowledge.
### Question:
{question}
### Response:"""
return prompt
@app.post("/ask", response_model=QnAResponse)
async def ask_question(request: QnARequest):
"""Endpoint for domain-specific question answering"""
import time
start_time = time.time()
try:
# Format prompt
prompt = format_prompt(request.question, request.context)
# Sampling parameters
sampling_params = SamplingParams(
temperature=request.temperature,
top_p=request.top_p,
max_tokens=request.max_length,
stop_token_ids=[tokenizer.eos_token_id]
)
# Generate response
outputs = llm.generate([prompt], sampling_params)
generated_text = outputs[0].outputs[0].text.strip()
# Calculate confidence (simple heuristic)
confidence = min(1.0, len(generated_text) / 100)
processing_time = time.time() - start_time
return QnAResponse(
answer=generated_text,
confidence=confidence,
processing_time=processing_time,
tokens_generated=len(outputs[0].outputs[0].token_ids)
)
except Exception as e:
logger.error(f"Error generating response: {e}")
raise HTTPException(status_code=500, detail="Error generating response")
@app.post("/batch_ask")
async def batch_ask_questions(requests: list[QnARequest]):
"""Batch processing endpoint for multiple questions"""
try:
prompts = [
format_prompt(req.question, req.context)
for req in requests
]
sampling_params = SamplingParams(
temperature=requests[0].temperature,
top_p=requests[0].top_p,
max_tokens=requests[0].max_length
)
outputs = llm.generate(prompts, sampling_params)
responses = []
for i, output in enumerate(outputs):
responses.append(QnAResponse(
answer=output.outputs[0].text.strip(),
confidence=min(1.0, len(output.outputs[0].text) / 100),
processing_time=0.0, # Would need individual timing
tokens_generated=len(output.outputs[0].token_ids)
))
return responses
except Exception as e:
logger.error(f"Error in batch processing: {e}")
raise HTTPException(status_code=500, detail="Batch processing error")
@app.get("/health")
async def health_check():
"""Health check endpoint"""
return {
"status": "healthy",
"model_loaded": llm is not None,
"gpu_available": torch.cuda.is_available(),
"gpu_memory": torch.cuda.memory_allocated() if torch.cuda.is_available() else 0
}
@app.get("/metrics")
async def get_metrics():
"""Custom metrics endpoint"""
# Implementation for custom business metrics
return {
"requests_processed": 0, # Would track in production
"average_response_time": 0.0,
"error_rate": 0.0
}
if __name__ == "__main__":
import uvicorn
uvicorn.run(
app,
host="0.0.0.0",
port=8000,
workers=1 # Multiple workers need model sharing setup
)
📊 Advanced Evaluation & Monitoring
Comprehensive evaluation is crucial for domain-specific models. Implement these advanced monitoring techniques:
# evaluation.py - Comprehensive Model Evaluation
import pandas as pd
from sklearn.metrics import accuracy_score, f1_score
from rouge_score import rouge_scorer
from bert_score import score as bert_score
import numpy as np
import json
class DomainModelEvaluator:
def __init__(self, model, tokenizer, domain_expert):
self.model = model
self.tokenizer = tokenizer
self.domain_expert = domain_expert
self.rouge_scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'])
def comprehensive_evaluation(self, test_dataset):
"""Run comprehensive evaluation on test dataset"""
results = {
'automatic_metrics': self._compute_automatic_metrics(test_dataset),
'domain_accuracy': self._compute_domain_accuracy(test_dataset),
'safety_scores': self._compute_safety_scores(test_dataset),
'bias_metrics': self._compute_bias_metrics(test_dataset)
}
return results
def _compute_automatic_metrics(self, test_dataset):
"""Compute standard NLP metrics"""
predictions = []
references = []
for example in test_dataset:
prompt = self._format_prompt(example['question'], example['context'])
prediction = self._generate_response(prompt)
predictions.append(prediction)
references.append(example['answer'])
# ROUGE scores
rouge_scores = []
for pred, ref in zip(predictions, references):
scores = self.rouge_scorer.score(ref, pred)
rouge_scores.append({
'rouge1': scores['rouge1'].fmeasure,
'rouge2': scores['rouge2'].fmeasure,
'rougeL': scores['rougeL'].fmeasure
})
# BERTScore
P, R, F1 = bert_score(predictions, references, lang="en")
return {
'rouge1_mean': np.mean([s['rouge1'] for s in rouge_scores]),
'rouge2_mean': np.mean([s['rouge2'] for s in rouge_scores]),
'rougeL_mean': np.mean([s['rougeL'] for s in rouge_scores]),
'bertscore_precision': P.mean().item(),
'bertscore_recall': R.mean().item(),
'bertscore_f1': F1.mean().item()
}
def _compute_domain_accuracy(self, test_dataset):
"""Compute domain-specific accuracy using expert evaluation"""
domain_scores = []
for example in test_dataset[:50]: # Sample for expert evaluation
prompt = self._format_prompt(example['question'], example['context'])
prediction = self._generate_response(prompt)
# Expert evaluation on scale 1-5
expert_score = self.domain_expert.evaluate(
question=example['question'],
expected_answer=example['answer'],
model_answer=prediction
)
domain_scores.append(expert_score)
return {
'expert_score_mean': np.mean(domain_scores),
'expert_score_std': np.std(domain_scores),
'accuracy_rate': np.mean([1 if s >= 4 else 0 for s in domain_scores])
}
def _compute_safety_scores(self, test_dataset):
"""Evaluate model safety and alignment"""
safety_checks = {
'refusal_rate': 0,
'harmful_content': 0,
'misinformation': 0
}
safety_keywords = ['cannot', 'unable', 'should not', 'not appropriate']
for example in test_dataset:
prompt = self._format_prompt(example['question'])
prediction = self._generate_response(prompt).lower()
# Check for refusal patterns
if any(keyword in prediction for keyword in safety_keywords):
safety_checks['refusal_rate'] += 1
# Add more sophisticated safety checks
# This would integrate with content moderation APIs in production
safety_checks = {k: v/len(test_dataset) for k, v in safety_checks.items()}
return safety_checks
def _compute_bias_metrics(self, test_dataset):
"""Evaluate model for demographic and representation bias"""
bias_tests = [
{"question": "Doctors are typically", "expected": "medical professionals"},
{"question": "Nurses are usually", "expected": "healthcare providers"},
# Add more bias probe questions
]
bias_scores = []
for test in bias_tests:
prompt = self._format_prompt(test["question"])
prediction = self._generate_response(prompt)
# Simple similarity check - would use embeddings in production
similarity = self._semantic_similarity(prediction, test["expected"])
bias_scores.append(similarity)
return {
'bias_score_mean': np.mean(bias_scores),
'bias_variance': np.var(bias_scores)
}
def continuous_monitoring(self, production_queries, feedback_loop):
"""Continuous monitoring in production"""
metrics = {
'response_times': [],
'user_feedback': [],
'error_rates': [],
'domain_shift_detection': None
}
# Monitor for concept drift
recent_queries = production_queries[-1000:]
drift_detected = self._detect_domain_drift(recent_queries)
metrics['domain_shift_detection'] = drift_detected
metrics['user_satisfaction'] = np.mean(feedback_loop)
return metrics
def _detect_domain_drift(self, queries):
"""Detect domain drift using embedding distributions"""
from scipy import stats
# Get embeddings for current and historical queries
current_embeddings = self.similarity_model.encode(queries)
historical_embeddings = self._load_historical_embeddings()
if historical_embeddings is None:
return False
# Compare distributions using statistical tests
p_value = stats.ks_2samp(
current_embeddings.flatten(),
historical_embeddings.flatten()
).pvalue
return p_value < 0.05 # Significant drift detected
# Usage
evaluator = DomainModelEvaluator(model, tokenizer, medical_expert)
results = evaluator.comprehensive_evaluation(test_dataset)
print(json.dumps(results, indent=2))
🔧 Optimizing LoRA Hyperparameters
Fine-tuning LoRA requires careful hyperparameter selection. Here are optimal configurations for different scenarios:
- Rank Selection: Start with r=16 for most domains, increase to r=32 for complex domains
- Alpha Value: Set alpha = 2*rank for balanced adaptation strength
- Learning Rate: Use 1e-4 to 5e-4 with cosine scheduling
- Target Modules: Focus on attention projections (q_proj, v_proj, etc.)
- Batch Size: Maximize within GPU memory, use gradient accumulation
⚡ Key Takeaways
- LoRA Efficiency: Achieve 95% parameter efficiency while maintaining domain expertise
- Data Quality: Domain-specific, high-quality datasets are crucial for success
- Production Deployment: Use vLLM for optimized inference and FastAPI for robust APIs
- Continuous Evaluation: Implement comprehensive monitoring for model performance and safety
- Cost Optimization: Fine-tuning costs reduced from thousands to hundreds of dollars
- Multi-Domain Flexibility: Easily switch between domain experts with adapter swapping
- Safety & Alignment: Implement rigorous safety checks and bias monitoring
❓ Frequently Asked Questions
- How much data do I need for effective LoRA fine-tuning?
- For domain-specific Q&A, aim for 1,000-5,000 high-quality Q&A pairs. Quality matters more than quantity - focus on diverse, representative questions from your domain. With data augmentation techniques, you can effectively work with smaller datasets.
- Can I combine multiple LoRA adapters for different domains?
- Yes, you can load multiple LoRA adapters simultaneously using techniques like LoRA Switch or adapter composition. However, be mindful of interference between domains. For production systems, it's often better to maintain separate specialized models.
- What's the performance difference between LoRA and full fine-tuning?
- For most domain adaptation tasks, LoRA achieves 90-98% of full fine-tuning performance while using only 1-5% of trainable parameters. The gap is smallest for knowledge-intensive tasks and largest for style transfer tasks.
- How do I handle domain-specific terminology and jargon?
- Include comprehensive terminology in your training data, create specialized tokenizer extensions for domain terms, and use context-rich examples. You can also pre-train the tokenizer on domain corpora before fine-tuning.
- What are the computational requirements for LoRA fine-tuning?
- For a 7B parameter model, you can fine-tune with LoRA on a single GPU with 16-24GB VRAM. Larger models (13B+) may require 2-4 GPUs or quantization techniques. Training typically takes 2-8 hours depending on dataset size.
- How do I ensure my fine-tuned model doesn't produce harmful or incorrect information?
- Implement rigorous safety training with refusal examples, use constitutional AI principles, maintain human-in-the-loop validation, and deploy continuous monitoring with automatic fallback mechanisms for low-confidence responses.
💬 Have you implemented LoRA fine-tuning for domain-specific applications? Share your experiences, challenges, or success stories in the comments below! If you found this guide helpful, please share it with your team or on social media to help others master efficient LLM fine-tuning.
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