In a previous article we discussed constraining output space for small language model (SLM) narrow automation optimization. We mentioned at the time that this was the first in a short series of SLM optimization strategy articles. We are now at the second of those. This time we will focus on the reuse of the prompt prefix with a key-value cache. Let's not waste any further time on the niceties and get right to it instead.

As in our previous article, all benchmarks below use Qwen2.5-0.5B-Instruct in float16 through Hugging Face Transformers, running on an M2 Macbook Air with 24GB RAM and a 16-core Neural Engine.

First, make sure you set up a Python environment and install your requirements:

pip install torch transformers accelerate

We will continue to use the support ticket framing from our first article.

Narrow automation prompts are relatively static. A task instruction, a taxonomy definition, and a few examples make up the bulk of the tokens, and only a short prompt tail changes from item to item. If your instruction block runs to a couple of hundred tokens and each ticket adds twenty or thirty, then the overwhelming majority of every prompt is byte-for-byte identical to the last one. It's clearly wasteful to be recomputing all of it, for every layer, on every call.

Transformers compute a key and a value vector for each token at each layer, and these depend only on the tokens to the left. This means that for a fixed prefix, they are identical on every call. Computing them once and holding onto them shrinks the per-item pre-fill down to just the tokens that actually changed.

First, the baseline: a realistic few-shot prompt, re-encoded in full for every ticket. The constrained scoring trick from our earlier article is carried forward here, so the decision is a single forward pass and the only thing left to optimize is the pre-fill. Note that the chat template is written out by hand rather than going through apply_chat_template(), because the next script needs to split the prompt at a known boundary.

import os import time import torch from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL_ID = "Qwen/Qwen2.5-0.5B-Instruct"

torch.set_num_threads(os.cpu_count() or 1)

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) model = AutoModelForCausalLM.from_pretrained(MODEL_ID, dtype=torch.float32) model.eval()

our toy data to classify (600 records)

LABELS = ["billing", "technical", "account"] tickets = [ "My card was charged twice for the same invoice.", "The mobile app crashes whenever I open the settings page.", "I need to change the email address on my profile.", ] * 200

constrained scoring, carried over from the previous article: the prompt ends at the

start of the assistant turn, so comparing the logits of each label's FIRST token

is enough to pick a winner, provided those first tokens are distinct

label_first_ids = [tokenizer.encode(label, add_special_tokens=False)[0] for label in LABELS] assert len(set(label_first_ids)) == len(LABELS), ( "Labels share a first token; score full label sequences instead." ) label_first_ids = torch.tensor(label_first_ids, device=model.device)

SYSTEM_PROMPT = """You classify customer support tickets into exactly one category.

Categories:

• billing: payments, invoices, refunds, charges, subscription costs
• technical: crashes, errors, performance problems, broken features
• account: profile changes, login access, permissions, account deletion

Examples: Ticket: I was billed twice in March. Category: billing Ticket: The dashboard never finishes loading. Category: technical Ticket: Please remove my old phone number from my profile. Category: account Ticket: My promo code was rejected at checkout. Category: billing Ticket: Exporting to CSV throws a 500 error. Category: technical Ticket: I cannot reset my password. Category: account """

the chat template is written out by hand so the prompt can be split at a known

token boundary; this is the exact ChatML layout Qwen2.5-Instruct expects

prefix_text = f"<|im_start|>system\n{SYSTEM_PROMPT}<|im_end|>\n"

def suffix_text(ticket): return ( f"<|im_start|>user\nTicket: {ticket}\nCategory:<|im_end|>\n" f"<|im_start|>assistant\n" )

def encode(text): return tokenizer(text, add_special_tokens=False)["input_ids"]

the split has to be token-clean: encoding the two halves separately must give

exactly the same ids as encoding the whole prompt in one go, or the cached keys

in the next script will not line up with what the model would otherwise have seen

_probe = suffix_text(tickets[0]) assert encode(prefix_text) + encode(_probe) == encode(prefix_text + _probe), ( "Prefix/suffix split is not token-clean; move the boundary." )

prefix_len = len(encode(prefix_text)) full_len = prefix_len + len(encode(_probe)) print(f"Static prefix length: {prefix_len} tokens") print(f"Full prompt length: {full_len} tokens ({100 * prefix_len / full_len:.0f}% of it static)")

time inference

baseline_predictions = [] start = time.time()

for n, ticket in enumerate(tickets, start=1):

this loop runs for minutes on CPU, so report progress rather than sitting silent

if n % 100 == 0: print(f" {n}/{len(tickets)} tickets ({(time.time() - start) / n:.2f}s each)", flush=True)

full = tokenizer( prefix_text + suffix_text(ticket), add_special_tokens=False, return_tensors="pt" ).to(model.device) with torch.no_grad(): logits = model(**full).logits[0, -1, :] baseline_predictions.append(LABELS[int(logits[label_first_ids].argmax())])

duration_full = time.time() - start

output task metrics

print(f"Recomputing the full prompt every time: {duration_full:.2f} seconds") print(f" ({1000 * duration_full / len(tickets):.1f} ms per ticket)")

Output:

Loading weights: 100%|████████████████████████████████████████████████████████████████████| 290/290 [00:01

Reusing the Prompt Prefix

We now run the prefix through the model exactly once, keep the resulting cache, and feed each item only its own tokens. This continues in the same script, so the model, the tokenizer, the prompt halves, and the baseline timing are all still in scope.

import os import time import torch from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache

MODEL_ID = "Qwen/Qwen2.5-0.5B-Instruct"

torch.set_num_threads(os.cpu_count() or 1)

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) model = AutoModelForCausalLM.from_pretrained(MODEL_ID, dtype=torch.float32) model.eval()

our toy data to classify (600 records)

LABELS = ["billing", "technical", "account"] tickets = [ "My card was charged twice for the same invoice.", "The mobile app crashes whenever I open the settings page.", "I need to change the email address on my profile.", ] * 200

constrained scoring, carried over from the previous article: the prompt ends at the

start of the assistant turn, so comparing the logits of each label's FIRST token

is enough to pick a winner, provided those first tokens are distinct

label_first_ids = [tokenizer.encode(label, add_special_tokens=False)[0] for label in LABELS] assert len(set(label_first_ids)) == len(LABELS), ( "Labels share a first token; score full label sequences instead." ) label_first_ids = torch.tensor(label_first_ids, device=model.device)

SYSTEM_PROMPT = """You classify customer support tickets into exactly one category.

Categories:

• billing: payments, invoices, refunds, charges, subscription costs
• technical: crashes, errors, performance problems, broken features
• account: profile changes, login access, permissions, account deletion

Examples: Ticket: I was billed twice in March. Category: billing Ticket: The dashboard never finishes loading. Category: technical Ticket: Please remove my old phone number from my profile. Category: account Ticket: My promo code was rejected at checkout. Category: billing Ticket: Exporting to CSV throws a 500 error. Category: technical Ticket: I cannot reset my password. Category: account """

the chat template is written out by hand so the prompt can be split at a known

token boundary; this is the exact ChatML layout Qwen2.5-Instruct expects

prefix_text = f"system\n{SYSTEM_PROMPT}\n"

def suffix_text(ticket): return ( f"user\nTicket: {ticket}\nCategory:\n" f"assistant\n" )

def encode(text): return tokenizer(text, add_special_tokens=False)["input_ids"]

the split has to be token-clean: encoding the two halves separately must give

exactly the same ids as encoding the whole prompt in one go, or the cached keys

will not line up with what the model would otherwise have seen

_probe = suffix_text(tickets[0]) assert encode(prefix_text) + encode(_probe) == encode(prefix_text + _probe), ( "Prefix/suffix split is not token-clean; move the boundary." )

prefix = tokenizer(prefix_text, add_special_tokens=False, return_tensors="pt").to(model.device) prefix_ids = prefix["input_ids"] prefix_len = prefix_ids.shape[1] full_len = prefix_len + len(encode(_probe)) print(f"Static prefix length: {prefix_len} tokens") print(f"Full prompt length: {full_len} tokens ({100 * prefix_len / full_len:.0f}% of it static)")

Populate the cache once with the static instruction block

prefix_cache = DynamicCache() with torch.no_grad(): model( input_ids=prefix_ids, attention_mask=torch.ones_like(prefix_ids), past_key_values=prefix_cache, use_cache=True, )

def classify_cached(ticket): suffix = tokenizer( suffix_text(ticket), add_special_tokens=False, return_tensors="pt" ).to(model.device) suffix_ids = suffix["input_ids"] suffix_len = suffix_ids.shape[1]

the mask must cover the cached prefix as well as the new tokens, and the new

tokens must be told they start at position prefix_len, not at position 0

attention_mask = torch.ones((1, prefix_len + suffix_len), device=model.device, dtype=torch.long) cache_position = torch.arange(prefix_len, prefix_len + suffix_len, device=model.device)

with torch.no_grad(): out = model( input_ids=suffix_ids, attention_mask=attention_mask, past_key_values=prefix_cache, cache_position=cache_position, use_cache=True, ) logits = out.logits[0, -1, :] label = LABELS[int(logits[label_first_ids].argmax())]

roll the cache back so the next ticket starts from the prefix alone

prefix_cache.crop(prefix_len)

return label

def classify_full(ticket): """Reference path: re-encode the whole prompt, no cache. Used only to verify.""" full = tokenizer( prefix_text + suffix_text(ticket), add_special_tokens=False, return_tensors="pt" ).to(model.device) with torch.no_grad(): logits = model(**full).logits[0, -1, :] return LABELS[int(logits[label_first_ids].argmax())]

correctness first: the cached path must agree with the uncached one on every

distinct ticket, otherwise the speedup below is measuring the wrong computation

mismatches = [t for t in dict.fromkeys(tickets) if classify_cached(t) != classify_full(t)] assert not mismatches, f"Cached path disagrees with full re-encoding on: {mismatches}" print(f"Cache verified against full re-encoding on {len(set(tickets))} distinct tickets")

time inference

predictions = [] start = time.time()

for n, ticket in enumerate(tickets, start=1):

this loop is fast, but report progress anyway so the two scripts look alike

if n % 100 == 0: print(f" {n}/{len(tickets)} tickets ({(time.time() - start) / n:.2f}s each)", flush=True)

predictions.append(classify_cached(ticket))

duration_cached = time.time() - start

output task metrics

print(f"Reusing the cached prefix: {duration_cached:.2f} seconds") print(f" ({1000 * duration_cached / len(tickets):.1f} ms per ticket)")

Output:

Static prefix length: 145 tokens Full prompt length: 167 tokens (87% of it static) Cache verified against full re-encoding on 3 dis