Skip the system prompt and the template writes one

I’d always assumed that if I leave the system prompt out, the model gets my message plus a few role markers round it, and that’s it. That’s what a chat template is for: the bit of Jinja that ships with a model and turns your list of messages into the one long string it actually reads.

So I sent Llama 3.1 8B Instruct a lone “Who made you?” through transformers, and printed the string:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

Cutting Knowledge Date: December 2023
Today Date: 26 Jul 2024

<|eot_id|><|start_header_id|>user<|end_header_id|>

Who made you?<|eot_id|><|start_header_id|>assistant<|end_header_id|>

That’s a system block that I didn’t write, telling the model that it’s July 2024. I checkec my watch - todays the 19th of October 2026.

Over a quarter of them add something

I took the 300 most downloaded text generation models on Hugging Face today and rendered a single user message through each one’s template, using transformers’ own renderer. 189 have a template in the repo (most of the rest are GGUF files, which keep theirs inside, or base models). Then I stripped out the role markers, the special tokens and my message, and looked at what was left.

60%12%15%13%

• Nothing60%
• "You are a helpful assistant", a thinking setting or similar12%
• "You are Qwen, created by Alibaba Cloud"15%
• Another name, maker, date or scope13%

114 add nothing, which is what I’d expected from all of them. 23 add something bland, mostly “You are a helpful assistant” or a line setting how hard to think. But the other 52 tell the model who it is, who made it, when its training stopped, what today’s date is, or what it’s allowed to talk about.

28 of those 52 are one line, Qwen2.5’s, repeated across its sizes and everybody’s quantised copies of it (Qwen3 dropped it). Lots of the rest are copies too, and if you count each of the 96 different templates once, it’s 20 of them that add a name, a date or a scope. So more like a fifth, but those are some of the biggest names on there:

Model	What it adds when you don’t
Qwen2.5 7B Instruct	You are Qwen, created by Alibaba Cloud. You are a helpful assistant.
gpt-oss 20B	You are ChatGPT, a large language model trained by OpenAI. Knowledge cutoff: 2024-06 Current date: 2026-10-19
Granite 3.3 2B	Knowledge Cutoff Date: April 2024. Today’s Date: October 19, 2026. You are Granite, developed by IBM.
Kimi K2 Instruct	You are Kimi, an AI assistant created by Moonshot AI.
DeepSeek Coder 7B v1.5	You only answer questions related to computer science. For politically sensitive questions, security and privacy issues… you will refuse to answer

So OpenAI’s open model calls itself ChatGPT, and a coding model is told not to talk about security. None of it’s hidden, it’s all in the repo, but you’d only know if you went and read the template.

July 2024 is written into Llama’s

Most templates that mention a date ask for today’s with strftime_now, a function whatever’s running the model is meant to supply. Meta’s Llama 3.1 and 3.3 templates do this instead:

{%- if not date_string is defined %}
    {%- set date_string = "26 Jul 2024" %}
{%- endif %}

That’s three days after 3.1 came out. If the runner passes a date the model gets that, and if it passes nothing it’s 26 July 2024 forever. That’s the official Llama 3.1 8B Instruct, downloaded 5.5 million times in the last month, and the quantised copies I checked carry the same file. Llama 3.2 switched to strftime_now, so it gets today.

So does anything pass a date? Same bare message, Meta’s 3.1 template, today:

Run with	Told the date is
transformers	26 Jul 2024
vLLM 0.32	26 Jul 2024
llama.cpp’s llama-server	19 Oct 2026
Ollama 0.40, ollama pull llama3.1	Nothing, no system block at all
Ollama 0.40, a GGUF you imported	19 Oct 2026

(No GPU here, so each runner got a tiny test model wearing the real template, and for vLLM that’s its template renderer called directly.) llama.cpp sets date_string to today on every render, and vLLM doesn’t set it anywhere. Ollama’s own llama3.1 isn’t Meta’s template at all but a Go one, which only opens a system block when you send a system prompt or tools. Import a GGUF that carries Meta’s Jinja, though, and Ollama hands it to its bundled llama-server, which puts today in.

Qwen2.5’s line came through transformers and llama-server the same, and Ollama’s qwen2.5 ships it as the model’s default system prompt.

Your own system prompt only fixes half of it

For Qwen2.5, passing any system message switches the default off, even an empty one:

<|im_start|>system
<|im_end|>

Llama 3.1 is different. Send “Be brief.” as the system prompt and the template keeps the stale date and puts yours underneath it:

Cutting Knowledge Date: December 2023
Today Date: 26 Jul 2024

Be brief.

Unless your runner fills it in like llama-server does, you have to pass date_string yourself. In transformers it’s another argument to apply_chat_template. vLLM takes it in chat_template_kwargs on the request, which it hands to the template:

{ "chat_template_kwargs": { "date_string": "19 Oct 2026" } }

The model mostly knew already

I wanted to see the DeepSeek Coder refusal for real, so I ran the 1.3B version (same line in its template) with the default and with an empty system message. It happily answered questions on storing passwords in Flask, SQL injection and setting up ufw either way, in nearly the same words. It turned down “What is the capital of France?” and a question about Tiananmen Square either way too, and without its line it said “as an AI focused on history, I don’t have the ability to provide information on historical events”. Qwen2.5 0.5B says it was made by Alibaba Cloud with or without its line.

So the names and the scopes are mostly belt and braces. The model learned them in training, and the template just repeats them again.

The date’s different though, since no model can know what day it is. I gave Llama 3.2 3B six questions that hang on the date, under the block Meta’s 3.1 template writes, the same block with today’s date, and no block. Most answers ignored the date whichever way, and it worked out someone’s age “as of 2023” all three times. The one that moved was a one-line email greeting mentioning the season. July 2024 got “the warm and sunny summer season”, today got “the crisp autumn season”, and no date got a helpful “[current season]”.

That’s a small model and one answer in six, but it’s the one line in these templates that’s news to the model, and it’s the one the runners can’t agree on.

See what yours sends

In transformers, print the template’s output before it’s tokenised:

from transformers import AutoTokenizer

tok = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
print(tok.apply_chat_template([{"role": "user", "content": "hi"}], tokenize=False))

And llama-server will show you what it’s about to send:

curl localhost:8080/apply-template \
  -d '{"messages": [{"role": "user", "content": "hi"}]}'

So… if there’s a date in in your response, check it’s actually today’s.