Fine-Tuning Llama 2 70B using PyTorch

1. Introduction

“Fine-tuning a 70B parameter model? That sounds like a terrible idea.”

At least, that’s what I thought when I first started working with Llama 2 70B. Let’s be real—handling a model of this scale isn’t just about slapping some GPUs together and running a few training loops.

If you don’t plan your approach right, you’ll be stuck in Out-of-Memory (OOM) hell, burning through compute credits while barely making progress.

Why fine-tune Llama 2 70B?

I’ve worked with smaller models before—7B, 13B, even 30B—but when you need state-of-the-art performance for complex domain-specific tasks, scaling up makes a difference. Here’s what I’ve found:

Massive improvements in domain adaptation – Whether it’s financial predictions or legal text generation, a properly fine-tuned Llama 2 70B blows smaller models out of the water.
Emergent reasoning abilities – Llama 2 70B isn’t just a bigger version of 13B; it picks up on nuances, context switching, and even logical reasoning that smaller models struggle with.
Customization over proprietary models – Why settle for OpenAI or Anthropic’s black-box APIs when you can own a powerful model fine-tuned exactly for your needs?

But here’s the catch—fine-tuning a 70B model isn’t like training a BERT classifier.

Challenges in Fine-Tuning a 70B Model

I’ve faced (and solved) enough roadblocks to know what will and won’t work. Here’s what you need to consider before jumping in:

🔹 Hardware Limitations – Even with 4x A100s, you’re looking at serious VRAM bottlenecks. A single FP16 forward pass on 70B eats ~80GB of memory. If you don’t optimize for LoRA, quantization, or DeepSpeed, you’re not making it past step one.

🔹 Data Pipeline Bottlenecks – Feeding data efficiently to a model this size is just as crucial as optimizing the model itself. If you’re not using streaming datasets, memory-mapped files, or efficient tokenization, your training speed tanks.

🔹 Training Instability – Unlike smaller models, fine-tuning Llama 2 70B can result in mode collapse or catastrophic forgetting. Tweaking learning rates, warm-up steps, and gradient checkpointing is non-negotiable.

What This Guide Covers

This isn’t going to be another generic “Fine-Tuning LLMs 101” post. I’m walking you through an end-to-end fine-tuning workflow—from environment setup to dataset preprocessing, training with DeepSpeed & LoRA, and deploying the model efficiently.

Expect real code, practical optimizations, and hard-earned lessons from fine-tuning massive models.

Let’s get started. 🚀

2. Setting Up the Environment

“Give me six hours to chop down a tree, and I will spend the first four sharpening the axe.” – Abraham Lincoln.

If you’re fine-tuning Llama 2 70B, your hardware setup is your axe—get it wrong, and no amount of tweaking will save you from slow, unstable training.

Hardware Requirements

I’ve trained Llama 2 70B on a few different setups. Here’s what works (and what doesn’t).

🔹 Minimum viable setup: 4x A100 80GB (or better)

Anything less, and you’ll need extreme memory optimizations (LoRA + quantization + FSDP).
Even with 4x A100s, full fine-tuning is barely feasible—you’ll likely need ZeRO-3 offloading.

🔹 If you’re using consumer GPUs (4090s):

Forget full fine-tuning. You’ll need QLoRA + bitsandbytes + offloading strategies.
Expect longer training times but still possible with efficient gradient accumulation.

🔹 TPUs?

I’ve tried TPU v4-512—great for inference, painful for training due to limited ecosystem support.
If you’re set on TPUs, JAX + FSDP is your best bet.

Estimating VRAM Requirements

This might save you some frustration:

Precision	Full Fine-Tuning	LoRA	QLoRA
FP32	🚫 4x A100s won’t cut it	🚫	🚫
FP16	✅ 8x A100s	✅ 4x A100s	✅ 2x 4090s
BF16	✅ More stable but same VRAM needs	✅	✅
8-bit (QLoRA)	🚀 ✅ Works on 1-2 consumer GPUs	🚀 ✅	🚀 ✅

🔹 My recommendation? Unless you’re running a multi-GPU H100 cluster, stick with LoRA or QLoRA.

Key Dependencies

Once you have the hardware sorted, let’s install only what’s necessary. No bloated dependencies.

pip install transformers accelerate bitsandbytes deepspeed

Configuring Accelerate for Multi-GPU Training

If you’re using multiple GPUs, PyTorch’s default behavior won’t distribute the model efficiently. Fix that by setting up Hugging Face’s accelerate:

accelerate config

Here’s the optimal config for 4x A100s:

{
  "compute_environment": "LOCAL_MACHINE",
  "distributed_type": "DEEPSPEED",
  "zero3_enable": true,
  "mixed_precision": "fp16"
}

💡 Pro Tip: If you’re low on VRAM, set "offload_param": { "device": "cpu" } to offload some layers to CPU.

3. Loading Llama 2 70B Efficiently

“If you think loading a 70B model is just a bigger version of loading a 7B model, you’re in for a rude awakening.”

I learned this the hard way when I first tried to fine-tune Llama 2 70B. Running from_pretrained() on a single GPU? Instant crash. Trying to fit it on a single A100? Out of memory. The truth is, handling Llama 2 70B requires more than just loading the model—you need an optimized approach right from the start.

Let me walk you through the best ways to do it.

Loading Llama 2 70B from Hugging Face

The first step is, of course, getting the model from Hugging Face. If you try to load it naively, it’s going to eat up 80GB+ VRAM instantly. But with the right setup, we can make it work smoothly.

Here’s how I do it:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "meta-llama/Llama-2-70b-hf"

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load the model efficiently
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,  # Use FP16 for lower memory usage
    device_map="auto"  # Automatically distribute across available GPUs
)

🔹 Why FP16? Because full precision (torch.float32) is a memory nightmare for 70B models. You’ll run out of VRAM immediately.
🔹 Why device_map="auto"? It ensures PyTorch spreads the model across multiple GPUs automatically.

That’s the bare minimum setup. But if you’re working with limited VRAM, you’ll need quantization and layer offloading—otherwise, this model won’t even fit.

Optimizing Memory with `bitsandbytes` Quantization

“Not enough GPU memory? No problem—just compress the model.”

One of the best hacks I’ve found for running Llama 2 70B on lower VRAM setups is 8-bit quantization using bitsandbytes. It cuts memory usage by nearly half with minimal performance loss.

Here’s how you do it:

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(
    load_in_8bit=True,  # Enable 8-bit quantization
    llm_int8_threshold=6.0  # Threshold for mixed-precision operations
)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,  # Apply quantization
    device_map="auto"  # Automatically distribute across GPUs
)

🔹 Why llm_int8_threshold=6.0? This keeps critical layers in higher precision, preventing performance degradation.
🔹 How much memory does this save? Up to 45%, depending on your GPU architecture.

If you’re working with RTX 4090s or consumer GPUs, this is a game-changer. Without it, even two 24GB GPUs won’t be enough.

Offloading Layers to CPU (If You’re REALLY Low on VRAM)

“When you’re running out of memory, the only way forward is offloading.”

If you still don’t have enough VRAM, offload some layers to CPU. PyTorch and Hugging Face make this surprisingly easy:

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="balanced"  # Balances between GPU and CPU
)

🔹 What does device_map="balanced" do?

It keeps frequently used layers on GPU (fast).
It offloads less active layers to CPU (slow but better than crashing).

💡 Pro Tip: If you want full control, use device_map={"transformer.h.0": "cpu", "transformer.h.1": "cuda:0", ...} to manually assign layers to specific devices.

Final Thoughts

So, what’s the best approach? It depends on your setup:

Hardware	Best Loading Strategy
4x A100s (80GB each)	`torch_dtype=torch.float16, device_map="auto"`
2x 4090s (24GB each)	`bitsandbytes` 8-bit quantization
1x 3090 (24GB)	`bitsandbytes` 8-bit + CPU offloading
TPU setup	Consider JAX-based loading instead

If you’ve got unlimited compute, go for full precision. But if you’re like most of us—working within hardware constraints—quantization and offloading are your best friends.

4. Preparing the Dataset for Fine-Tuning

“Garbage in, garbage out.” That’s the first thing I learned when fine-tuning LLMs.

No matter how good your model is, if your dataset isn’t properly formatted, tokenized, and optimized for training, you’ll waste compute and get poor results.

In this section, I’ll walk you through how I prepare datasets for fine-tuning Llama 2 70B—from choosing the right format to efficient tokenization and streaming for large-scale data.

Choosing the Right Dataset Format

If you’re fine-tuning a massive model like Llama 2 70B, the way you store and process your dataset matters a lot. Here’s how different formats compare:

Format	Best For	Pros	Cons
JSONL	Text-based datasets	Simple, easy to parse	Slower to load, not memory efficient
Parquet	Large datasets	Compressed, fast loading	Slightly harder to edit
Hugging Face Datasets (`.arrow`)	Training pipelines	Streamable, optimized for ML	Requires `datasets` library

🔹 What do I use? For large-scale fine-tuning, I always prefer Hugging Face Datasets (.arrow or Parquet) because they support memory-efficient streaming and fast lookup. JSONL works too, but it’s not great for large datasets—loading it into memory can be painful.

Loading and Tokenizing the Dataset

Llama 2 70B requires properly formatted input sequences, so let’s start by loading the dataset and tokenizing it.

from datasets import load_dataset
from transformers import AutoTokenizer

# Load the dataset (replace with your actual dataset name)
dataset = load_dataset("your_dataset_name")

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-70b-hf")

# Tokenization function
def tokenize_function(examples):
    return tokenizer(
        examples["text"], 
        truncation=True, 
        padding="max_length", 
        max_length=2048  # Adjust as needed
    )

# Apply tokenization
tokenized_dataset = dataset.map(tokenize_function, batched=True)

🔹 Why use batched=True? It speeds up tokenization by processing multiple samples at once, instead of one by one.
🔹 Why max_length=2048? That’s Llama 2’s max token limit, but if you’re fine-tuning on shorter sequences, you can adjust it.

Memory-Efficient Dataset Loading (Streaming Mode)

“Ever tried loading a 100GB dataset into RAM? Yeah, don’t do that.”

If your dataset is too big to fit in memory, use streaming mode instead. This allows your model to load data on the fly without consuming unnecessary resources.

Here’s how I do it:

# Load dataset in streaming mode (no memory overhead)
dataset = load_dataset("your_dataset_name", streaming=True)

# Iterate over the dataset without loading it all into RAM
for sample in dataset["train"]:
    print(sample["text"])  # Process one sample at a time

print(sample["text"])  # Process one sample at a time

🔹 Why streaming? It’s a lifesaver when dealing with huge datasets.
🔹 What’s the trade-off? You can’t shuffle the entire dataset in advance, but Hugging Face Datasets handles shuffling within streamed batches.

Final Thoughts

Choosing the right dataset format and optimizing tokenization can drastically reduce memory usage and speed up fine-tuning.

🔹 If your dataset is small: JSONL or Hugging Face Datasets (.arrow) is fine.
🔹 If your dataset is large: Use Parquet format and streaming mode.
🔹 If you’re low on memory: Tokenize and save preprocessed datasets instead of doing it on-the-fly during training.

5. Fine-Tuning Llama 2 70B with PyTorch

“Training a 70B parameter model from scratch? Unless you own a supercomputer, don’t even think about it.”

Fine-tuning a model this big is no joke—it’s an engineering challenge that requires careful memory management, distributed training, and the right optimization techniques. I’ve spent hours debugging OOM errors, tweaking configurations, and figuring out what actually works in a real-world fine-tuning pipeline.

Let’s dive into the right approach for fine-tuning Llama 2 70B efficiently using PyTorch, DeepSpeed, and LoRA.

Choosing the Right Fine-Tuning Approach

When fine-tuning a massive model like Llama 2 70B, you have three options:

Method	Pros	Cons	Best For
Full Fine-Tuning	Maximum accuracy	Insanely expensive (costs hundreds of GBs of VRAM)	If you have TPUs/A100 clusters
LoRA (Low-Rank Adaptation)	Efficient, requires ~30% less VRAM	Slightly less flexible than full fine-tuning	Training on single/multi-GPU setups
QLoRA (Quantized LoRA)	Even lower memory footprint, 8-bit training	Requires bitsandbytes, performance trade-offs	Fine-tuning on consumer GPUs (RTX 4090/3090)

What do I recommend?

🔹 If you have powerful GPUs (A100, H100, TPUs): Use LoRA—it’s efficient without sacrificing too much accuracy.
🔹 If you’re limited to a single GPU (RTX 4090, etc.): Use QLoRA—it lets you fine-tune Llama 2 on 8-bit precision without crashing.

Implementing LoRA for Efficient Fine-Tuning

“Why update 70 billion parameters when you can update just a few thousand?”

LoRA (Low-Rank Adaptation) freezes most of the model’s weights and only trains a small set of additional parameters. This drastically reduces memory usage while still letting the model learn.

Here’s how I implement LoRA on Llama 2 70B:

from peft import get_peft_model, LoraConfig, TaskType

# Define LoRA config
lora_config = LoraConfig(
    task_type=TaskType.CAUSAL_LM,  # Causal language modeling
    r=8,  # Low-rank matrices
    lora_alpha=32,
    lora_dropout=0.1,
    target_modules=["q_proj", "v_proj"]  # Only train attention projections
)

# Apply LoRA to the model
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()  # Verify how many parameters are trainable

🔹 Why q_proj and v_proj? These layers control attention projections—they’re the best targets for LoRA fine-tuning in transformers.
🔹 Why r=8? This controls how many parameters LoRA adds. A lower r means less memory usage, but too low can hurt accuracy.

Using DeepSpeed for Distributed Training

“Your single GPU isn’t enough? Let’s put multiple to work.”

Fine-tuning Llama 2 70B on a single GPU is tough—even with LoRA. That’s where DeepSpeed and FSDP (Fully Sharded Data Parallel) come in.

Configuring DeepSpeed (Zero Redundancy Optimizer 3)

DeepSpeed’s Zero Redundancy Optimizer (ZeRO) splits model weights across multiple GPUs, reducing memory usage by 3x or more.

Here’s my DeepSpeed config (ds_config.json):

{
  "zero_optimization": {
    "stage": 3,
    "offload_param": { "device": "cpu" }, 
    "offload_optimizer": { "device": "cpu" }
  },
  "fp16": {
    "enabled": true
  }
}

🔹 Why ZeRO-3? It shards optimizer states across GPUs, making fine-tuning much more memory-efficient.
🔹 Why offload to CPU? If you’re limited on GPU VRAM, this helps reduce memory spikes during training.

To launch training with DeepSpeed, use:

deepspeed --num_gpus=4 train.py --deepspeed ds_config.json

Training Script Using PyTorch Trainer

Now, let’s put everything together into a training script. I use Hugging Face’s Trainer API because it handles gradient accumulation, checkpoints, and logging automatically.

from transformers import TrainingArguments, Trainer

# Define training arguments
training_args = TrainingArguments(
    output_dir="./results",  # Save model checkpoints
    per_device_train_batch_size=2,  # Batch size per GPU
    gradient_accumulation_steps=8,  # Simulates a larger batch size
    optim="adamw_bnb_8bit",  # Optimized 8-bit Adam optimizer
    fp16=True,  # Mixed precision training
    logging_steps=10,  
    save_steps=1000,  
    evaluation_strategy="steps",  
    save_total_limit=2  # Keep only last 2 checkpoints
)

# Initialize trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset
)

# Start training
trainer.train()

🔹 Why gradient_accumulation_steps=8? It lets you simulate a larger batch size even if your GPU has limited memory.
🔹 Why optim="adamw_bnb_8bit"? It’s an optimized 8-bit Adam optimizer that reduces VRAM usage significantly.
🔹 Why fp16=True? Mixed precision speeds up training without sacrificing much accuracy.

Final Thoughts

Fine-tuning Llama 2 70B isn’t just about running a script—it’s about optimizing every step to prevent memory issues and maximize efficiency.

What I’ve learned from experience:

LoRA is a game-changer—you don’t need full fine-tuning unless you have insane compute.
DeepSpeed ZeRO-3 makes multi-GPU training manageable—otherwise, you’ll hit memory limits fast.
Gradient accumulation and mixed precision training are essential—they let you fine-tune even with limited hardware.

6. Evaluation & Inference

“Fine-tuning a model is one thing, but how do you know it actually works?”

I’ve seen many data scientists train massive models, only to realize they have no solid way to evaluate them. If you don’t measure performance properly, you’re just hoping your fine-tuning worked. And hope is not a strategy.

In this section, I’ll show you how to measure fine-tuning performance using perplexity and then optimize inference for real-world usage.

Measuring Fine-Tuning Performance Using Perplexity

“How do you measure a language model’s performance? Accuracy? Loss? Nope—it’s all about perplexity.”

Perplexity (PPL) is a common metric for language models that essentially tells you:

Lower perplexity = better predictions.
Higher perplexity = more randomness, worse performance.

I always compute perplexity after fine-tuning to make sure the model actually learned something useful. Here’s how you can do it:

import torch
from torch.nn import functional as F

def compute_perplexity(model, tokenizer, text):
    inputs = tokenizer(text, return_tensors="pt").to("cuda")  # Tokenize input
    with torch.no_grad():
        outputs = model(**inputs)  # Get model output
        loss = F.cross_entropy(
            outputs.logits[:, :-1].reshape(-1, outputs.logits.shape[-1]), 
            inputs["input_ids"][:, 1:].reshape(-1)
        )  # Compute cross-entropy loss

    return torch.exp(loss)  # Convert loss to perplexity

print(compute_perplexity(model, tokenizer, "This is a test sentence."))

🔹 Why cross-entropy loss? It measures how well the model predicts the next token.
🔹 Why use torch.exp(loss)? Because perplexity is just the exponentiation of loss.

What’s a good perplexity score?

< 20 → Great (highly fluent model)
20-50 → Decent (usable, but room for improvement)
> 50 → Poor (your model might be generating garbage)

Inference with Optimized Settings

“You’ve trained your model—now what?”

Fine-tuning isn’t the end of the journey. You need optimized inference settings to generate high-quality text without wasting compute power.

Here’s how I run inference efficiently:

prompt = "What are the effects of quantum computing on cryptography?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")  # Move input to GPU

output = model.generate(
    **inputs, 
    max_length=300,  
    do_sample=True,  # Enable sampling for more natural responses
    top_p=0.9,  # Use nucleus sampling
    temperature=0.7  # Control randomness
)

print(tokenizer.decode(output[0], skip_special_tokens=True))

Breaking it down:

✔ do_sample=True → Enables stochastic sampling for more diverse outputs.
✔ top_p=0.9 → Uses nucleus sampling, which keeps only the most likely tokens. This avoids bland, repetitive text.
✔ temperature=0.7 → Lowers randomness, making responses more controlled.

Pro Tip: If you want factual responses, set temperature=0.3. If you want creative text, increase it to 0.8+.

7. Deployment Considerations

“A great model is useless if it’s too slow to deploy.”

If you’re running inference on Llama 2 70B without optimizations, you’re doing it wrong. The model is huge, and without proper techniques, inference will be painfully slow and expensive.

Quantization for Faster Inference (GPTQ, AWQ, BitsAndBytes)

“What if I told you… you don’t need full 16-bit precision?”

Quantization shrinks the model’s precision (e.g., from 16-bit to 8-bit or 4-bit), dramatically reducing memory usage without major performance loss.

🔹 Which quantization method should you use?

Method	Pros	Cons	Best For
GPTQ	Best balance of speed & accuracy	Needs extra setup	Cloud/enterprise inference
AWQ (Activation-aware Quantization)	Minimal accuracy loss	Slightly slower than GPTQ	Production-ready models
BitsAndBytes (bnb)	Easiest to use, great for GPUs	Slightly lower quality	Consumer GPUs

Here’s how you quantize Llama 2 70B using BitsAndBytes:

from transformers import BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,  # 4-bit quantization
    bnb_4bit_compute_dtype=torch.float16  # Mixed precision for efficiency
)

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-70b-hf",
    quantization_config=bnb_config,
    device_map="auto"  # Auto-allocate to available devices
)

✔ 4-bit quantization reduces memory usage by ~75%
✔ Works well on consumer GPUs (RTX 3090/4090)

Serving the Model Using vLLM for High-Throughput Inference

“Slow inference kills user experience.”

For real-world deployment, you cannot rely on Hugging Face’s .generate() for large-scale applications—it’s too slow. Instead, I recommend vLLM for high-throughput inference.

Why vLLM?

5x faster inference using PagedAttention.
Supports continuous batching (handles multiple users efficiently).
Easy to set up and deploy.

How to deploy Llama 2 70B with vLLM:

First, install vLLM:

pip install vllm

Then, launch the API server:

python -m vllm.entrypoints.api_server --model meta-llama/Llama-2-70b-hf

Now, you can call the model via API in your application:

import requests

response = requests.post(
    "http://localhost:8000/generate",
    json={"prompt": "Explain quantum physics in simple terms.", "max_tokens": 200}
)

print(response.json()["text"])

Using Triton Inference Server for Scalable Deployments

“Need to handle thousands of users? Use Triton.”

If you’re deploying at scale, NVIDIA’s Triton Inference Server is the best option. It supports:
Multi-GPU/multi-node inference
Model parallelism (ideal for Llama 2 70B)
Auto-batching for increased efficiency

To deploy Llama 2 70B on Triton:

1️⃣ Install NVIDIA Triton:

docker pull nvcr.io/nvidia/tritonserver:latest

2️⃣ Run Triton with your model:

docker run --gpus=all --rm -p 8000:8000 nvcr.io/nvidia/tritonserver:latest

3️⃣ Call the model via API:

import requests

response = requests.post("http://localhost:8000/v2/models/llama2/infer", json={
    "inputs": [{"name": "input", "shape": [1, 1024], "datatype": "FP16", "data": [YOUR_INPUT_TOKENS]}]
})

print(response.json())

Conclusion: Bringing It All Together

“Fine-tuning Llama 2 70B is both an art and a science.”

You’ve seen firsthand how dataset preparation, fine-tuning, evaluation, and deployment all play a role in building a high-performance model.

But let’s be honest—this is just the beginning. The real magic happens when you start experimenting, optimizing, and pushing the limits of what’s possible.

Summary of Key Points

Dataset Preparation Matters: Choose the right format (JSONL, HF datasets, Parquet) and use efficient tokenization + chunking strategies.
Fine-Tuning Approaches: Full fine-tuning is expensive, so techniques like LoRA and QLoRA are the go-to methods for adapting large models.
Performance Evaluation: Perplexity (PPL) is your best friend—if your PPL is too high, your fine-tuning didn’t work.
Optimized Inference is Key: Use top-p sampling, temperature tuning, and quantization (GPTQ, AWQ, BitsAndBytes) for faster, cheaper inference.
Scaling Deployment: Hugging Face .generate() won’t cut it for production—use vLLM or Triton for high-throughput inference.

Bottom line?
Training is easy. Making a model perform well in real-world scenarios is the real challenge.

Amit Yadav

I’m a Data Scientist.

1. Introduction

Why fine-tune Llama 2 70B?

Challenges in Fine-Tuning a 70B Model

What This Guide Covers

2. Setting Up the Environment

Hardware Requirements

Estimating VRAM Requirements

Key Dependencies

Configuring Accelerate for Multi-GPU Training

3. Loading Llama 2 70B Efficiently

Loading Llama 2 70B from Hugging Face

Optimizing Memory with bitsandbytes Quantization

Offloading Layers to CPU (If You’re REALLY Low on VRAM)

Final Thoughts

4. Preparing the Dataset for Fine-Tuning

Choosing the Right Dataset Format

Loading and Tokenizing the Dataset

Memory-Efficient Dataset Loading (Streaming Mode)

Final Thoughts

5. Fine-Tuning Llama 2 70B with PyTorch

Choosing the Right Fine-Tuning Approach

What do I recommend?

Implementing LoRA for Efficient Fine-Tuning

Using DeepSpeed for Distributed Training

Configuring DeepSpeed (Zero Redundancy Optimizer 3)

Training Script Using PyTorch Trainer

Final Thoughts

What I’ve learned from experience:

6. Evaluation & Inference

Measuring Fine-Tuning Performance Using Perplexity

Inference with Optimized Settings

Breaking it down:

7. Deployment Considerations

Quantization for Faster Inference (GPTQ, AWQ, BitsAndBytes)

Serving the Model Using vLLM for High-Throughput Inference

How to deploy Llama 2 70B with vLLM:

Using Triton Inference Server for Scalable Deployments

Conclusion: Bringing It All Together

Summary of Key Points

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Optimizing Memory with `bitsandbytes` Quantization