Chatbot Training Dataset Breakdown | Generated by AI
Question: Explain this SFT data mixture and the special tokens like <|user_start|>, <|user_end|>, <|assistant_start|>, and <|assistant_end|>.
Answer:
This table describes the Supervised Fine-Tuning (SFT) dataset used to teach a pretrained language model how to behave as a chatbot. After pretraining (predicting the next token on large amounts of text), the model is fine-tuned on high-quality instruction-response examples.
The total SFT dataset contains 1,071,759 conversation examples.
| Dataset | Rows | Why it’s included |
|---|---|---|
| SmolTalk | 460K | General conversations, Q&A, helpful assistant behavior |
| MMLU ×3 | 300K | Logical reasoning and knowledge questions |
| SimpleSpelling | 200K | Learn spelling corrections |
| SpellingBee | 80K | Count letters in words (a task LLMs often struggle with) |
| GSM8K ×4 | 32K | Elementary math word problems |
| Identity | 1K | Teach the model its name, creator, and persona |
| Custom code + SEC | 1K | Specialized programming and SEC/financial conversations |
Let’s examine each one.
1. SmolTalk (460K)
This is the largest portion.
It contains ordinary chatbot conversations, for example:
User: Explain recursion.
Assistant: Recursion is when a function calls itself...
The model learns:
- answer questions
- maintain conversations
- be polite
- follow instructions
- refuse unsafe requests appropriately
This is what primarily turns a language model into a chatbot.
2. MMLU ×3 (300K)
MMLU stands for Massive Multitask Language Understanding.
It contains multiple-choice questions such as:
Question:
Which planet is closest to the Sun?
A. Venus
B. Mercury
C. Earth
D. Mars
Correct answer:
B. Mercury
The ×3 usually means the dataset has been repeated or augmented three times so it has more influence during training.
Without repeating it, the model might not spend enough updates learning reasoning tasks.
3. SimpleSpelling (200K)
Examples:
User:
Correct this spelling:
recieve
Assistant:
receive
or
Correct:
acomodation
Assistant:
accommodation
This improves spelling accuracy.
4. SpellingBee (80K)
This dataset teaches counting letters.
Example:
User:
How many r's are in "strawberry"?
Assistant:
3
This seems simple, but token-based language models historically struggled with it because they reason over tokens, not individual characters.
Special training examples help improve this ability.
5. GSM8K ×4 (32K)
GSM8K is a well-known dataset of grade-school math problems.
Example:
Tom has 5 apples.
He buys 3 more.
How many apples does he have?
Answer:
8
Again, ×4 means they oversampled the dataset to give math problems more weight during training.
6. Identity (1K)
This teaches the assistant its identity.
Examples:
Who created you?
I was created by Company X.
or
What model are you?
I am XYZ-7B.
Without these examples, the model might answer inconsistently.
7. Custom code + SEC (1K)
These are manually curated conversations.
For example:
Programming:
Write a Python binary search.
Financial:
Explain an SEC 10-K filing.
Although there are only 1,000 examples, they are usually high quality and tailored to the project’s goals.
Why mix many datasets?
Each dataset teaches a different capability.
| Dataset | Skill learned |
|---|---|
| SmolTalk | Conversation |
| MMLU | Reasoning |
| GSM8K | Mathematics |
| SimpleSpelling | Spelling |
| SpellingBee | Character counting |
| Identity | Persona consistency |
| Custom | Domain expertise |
Instead of relying on one dataset, the model becomes stronger across multiple skills.
What are these special tokens?
<|user_start|>
<|user_end|>
<|assistant_start|>
<|assistant_end|>
These are special control tokens inserted into the training text to mark who is speaking.
For example, a conversation is converted into one continuous sequence:
<|user_start|>
What is Python?
<|user_end|>
<|assistant_start|>
Python is a programming language.
<|assistant_end|>
The model does not see separate “messages.” It only sees a sequence of tokens.
These special tokens tell it:
- where the user’s message begins
- where the user’s message ends
- where the assistant should begin responding
- where the assistant’s response ends
How SFT training works
Suppose one training example is:
<|user_start|>
Translate "hello" into French.
<|user_end|>
<|assistant_start|>
Bonjour.
<|assistant_end|>
During SFT:
- The entire conversation is fed into the transformer.
- The loss is typically computed only on the assistant’s tokens (
Bonjour.and the end marker). - The user tokens provide context but are masked out from the training loss.
Conceptually:
Input:
<User> Translate "hello" into French.
Target:
Bonjour.
This teaches the model to generate the assistant’s response given the preceding conversation.
Relationship to pretraining
Pretraining uses plain text such as:
The capital of France is Paris.
and trains the model to predict the next token everywhere.
SFT instead uses structured conversations:
<|user_start|>
Explain attention.
<|user_end|>
<|assistant_start|>
Attention lets each token selectively focus on other tokens...
<|assistant_end|>
The objective is still next-token prediction, but now the model learns the pattern:
User message → Assistant reply
rather than simply continuing arbitrary internet text.
This is why SFT is the stage that transforms a general language model into an instruction-following conversational assistant.