LLM Safety and Security Stack
Perfect 👍 — let’s extend the **weights vs. architecture** idea into **fine-tuning** and **adapters (like LoRA, PEFT, etc.)**, since that’s where the distinction really matters.
---
# 🔹 Fine-tuning and adapters in the context of LLMs
## 1. Full fine-tuning
* **What happens?**
You start from the pretrained model (architecture + big checkpoint of weights).
Then you run additional training on your domain/task data (e.g., medical texts).
* **Effect:**
* Updates **all weights** in the network (billions of them).
* Produces a *new checkpoint file* (still same architecture, but new numbers).
* **Downsides:**
* Extremely costly (GPU/TPU scale).
* Storage: you must keep the full new weight file (tens of GBs).
👉 Here, the **architecture stays the same**, but the **weights change everywhere**.
---
## 2. Parameter-efficient fine-tuning (PEFT)
Researchers realized: you don’t need to update all billions of weights — just adjust a small number of extra parameters.
### Example: **LoRA (Low-Rank Adaptation)**
* Idea: instead of training full weight matrices, insert small low-rank matrices ($A, B$) that approximate the weight updates.
* During inference:
$$
W' = W + A B
$$
where $W$ = frozen pretrained weights, $A, B$ = small trainable matrices.
* **Only A and B are trained**, so they’re tiny (MBs vs GBs).
* To deploy: load base model weights + LoRA adapter.
### Other PEFT methods
* **Prefix Tuning / Prompt Tuning**: train special “prefix embeddings” that steer the model.
* **Adapters**: add small new feedforward layers in between frozen transformer blocks.
---
## 3. Why this matters for "what is the model?"
* **Base model**: architecture + giant pretrained weights.
* **LoRA adapter**: just a small add-on weight file (e.g., 50MB vs 13GB).
* **Final usable model** = base weights + adapter loaded into the architecture.
So:
* If someone says “I trained a new model with LoRA,” what they really have is:
* the **base model** (frozen), plus
* a **tiny adapter file** that modifies behavior.
---
## 4. Analogy (building on the game analogy earlier 🎮)
* **Base model weights** = the original game data file (vanilla progress).
* **Architecture** = game engine.
* **LoRA adapter** = a *mod* (small patch file) that changes gameplay (like a custom skin, new rules).
* Final “modded game” = engine + vanilla save + patch applied at load time.
---
## ✅ Summary
* **Fine-tuning** = retrains all weights → new checkpoint.
* **LoRA/PEFT** = keep base weights frozen, add small trainable “patches.”
* **Model** in practice = architecture + (base weights + optional adapters).
This is why when people share LoRA adapters on Hugging Face, they’re just tiny files — you must also download the full base model to actually run them.
---
👉 Do you want me to also show a **short PyTorch sketch of how LoRA works inside a linear layer** (i.e., how $W' = W + AB$ is applied during forward pass)? That’s often the “aha!” moment for engineers.
Alright, let’s peel back the curtain and actually see **how LoRA works inside a single linear layer**.
The key is simple: instead of updating the huge weight matrix $W$ directly, we freeze $W$ and inject a **low-rank update** of the form $AB
$.-
--# 🔹 LoRA inside a Linear Lay
erSuppose a transformer has a linear laye
r:
y = W x, \quad W \in \mathbb{R}^{d_{\text{out}} \times d_{\text{in}}}
$$
$$Normally, fine-tuning means updating all entries of $W$. With **LoRA*
*:
W' = W + \Delta W, \quad \Delta W = A B
$$
$$whe
re* $A \in \mathbb{R}^{d_{\text{out}} \times r
* $B \in \mathbb{R}^{r \times d_{\text{in}}}$
* $r \ll \min(d_{\text{in}}, d_{\text{out}})$ (small rank, e.g., 4, 8, 16).
}$So the forward pass become
s:
y = (W + AB) x = W x + A (B x).
$$
$$* $W$ is **frozen** (not trained
* Only $A$ and $B$ are trained.
).That’s i
t!-
--# 🔹 Pseudocode (PyTorch-styl
e)```pyth
import torch
import torch.nn as nn
onclass LoRALinear(nn.Module
def __init__(self, d_in, d_out, r=8, alpha=16):
super().__init__()
self.d_in = d_in
self.d_out = d_out
self.r = r
): # Frozen base weig
self.W = nn.Parameter(torch.randn(d_out, d_in), requires_grad=False)
ht # Trainable low-rank facto
self.A = nn.Parameter(torch.randn(d_out, r)) # small matrix
self.B = nn.Parameter(torch.randn(r, d_in)) # small matrix
rs # Scaling (LoRA uses alpha/r to keep updates stabl
self.scaling = alpha / r
e) def forward(self, x
base = x @ self.W.T # frozen base
update = (x @ self.B.T) @ self.A.T * self.scaling
return base + update
```
):-
--# 🔹 What’s happening her
e?* `self.W`: pretrained frozen weights (GBs of parameters
* `self.A, self.B`: small trainable matrices (MBs).
* `forward`: applies both the frozen base and the low-rank update.
).At inference tim
e:* Load `W` from the base model (frozen
* Load `A,B` from the LoRA adapter file.
* Compute `Wx + ABx` in forward pass.
).Storage cost = base checkpoint (e.g. 13GB for LLaMA-2-7B) + tiny LoRA adapter (say 50MB
).-
--# 🔹 Analo
gyThink of LoRA as a **sticker overlay** on a big poste
r:* The poster (base model weights) is unchange
* The stickers (LoRA updates) tweak small details.
* Together, you see a modified picture — but the sticker file is tiny compared to the poster.
d.-
--✅ This is the exact trick that makes LoRA so practical: instead of retraining billions of parameters, you just train a few millio
n.-
--Would you like me to **expand this into a mini working example** where we create a toy base model and fine-tune it with LoRA on some dummy data, so you can see the parameters updat
e?