Follow this message
S01 · It starts with a message
Prepared reply — written by our team
Imposed choices, recorded calculations. No live model call.
The same message leaves the application; no answer has been added.
Read the prepared reply (outside the model input)
Hi, I will be a little late. Sorry to keep you waiting!
What am I talking to when I type a message?
You type into an application. It is the interface, not the language model itself.
The reply is authored; model calculations along the imposed path were recorded separately. No live model call.
- YouA prepared request.
- ApplicationThe screen where you type.
- Your messageThe request, not the reply.
The reply is authored; model calculations along the imposed path were recorded separately. No live model call.
Show me
Will the reply already be sitting inside this message?
The application prepares a request. Producing the reply is a separate step.
Go deeper
This example follows a remotely hosted assistant. The person, computer and journey are teaching diagrams, not recordings of a real conversation.
S02 · The request reaches a server
Does the model receive only the text I can see?
A server is a computer running software. The model is the learned calculation that software uses; it is not the server itself.
The application prepares the input. Open this example's envelope to see exactly what was included.
- ApplicationPrepares the input.
- ServerThe hosting computer.
- ModelThe learned calculation, run here.
Recorded input — the authored target remains outside the initial request.
Show me
Could the input contain more than the visible message?
- User message
- Write a message to let someone know I will be late.
- Implicit system from the official template
- You are a helpful AI assistant named SmolLM, trained by Hugging Face
- History / tools
- Absent in this example.
Inspect the complete input
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant
Roles and exact template
[
{
"role": "user",
"content": "Write a message to let someone know I will be late."
}
]{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
' }}{% endif %}{{'<|im_start|>' + message['role'] + '
' + message['content'] + '<|im_end|>' + '
'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
' }}{% endif %}im_start / im_end delimit roles; the assistant prefix opens the continuation. No message normalization; nothing omitted from the serialization above.
What the application sends and what you see in its message bubble need not be identical.
Go deeper
Show the complete serialized input as well as readable sections. If instructions or history are absent, say absent in this example. Special markers and omitted formatting must be identified.
Other systems run models locally. This diagram does not describe every network or every provider's privacy policy.
S03 · The model was trained before this message
What had already been learned before you pressed Send?
A large language model, or LLM, is a computer model trained to process and produce text.
During training, examples, predictions and measured errors are used to adjust numbers called parameters. During this reply, those learned numbers stay fixed.
- Before your messageTraining adjusts the parameters.
- Trained modelLearned parameters, not stored answer cards.
- NowParameters stay fixed during this reply.
Training remains a teaching diagram; the later inference calculations use fixed parameters.
Show me
Does this new message retrain the model?
Before this conversation: training
◇ Teaching diagram — simplified, not measured · □ □ ◇ → □ ◇ ◇
Example → prediction → measured error → parameter adjustment. These marks represent a past change, without invented training numbers.
◇ Teaching diagram — simplified, not measured
□ ◇ ◇ → 🔒 · Symbolic marks, not captured values.
During training, examples, predictions and measured errors are used to adjust numbers called parameters. During this reply, those learned numbers stay fixed.
Training changes the model's learned settings. Using them to process an input is called inference.
Go deeper
The training diagram shows one principle, not a capture of this model's training. A prediction is compared with an example's target; an optimization procedure adjusts parameters using a measured error.
Parameters are not one-word drawers or individual memories. Models can nevertheless memorize some training material. Additional training can improve instruction-following; it is not shown here.
S04 · Text becomes tokens
Does the model receive a list of whole words?
A tokenizer turns text into units called tokens and their numerical IDs. A token is not necessarily a whole word.
An ID points to an entry in that tokenizer's vocabulary. A larger number does not mean a more important word.
- Input textThe full envelope, not just the message bubble.
- TokenizerSplits according to its rules.
- Ordered tokensA piece, a position and an ID.
Verified tokenizer output — exact fragments and IDs, not invented for the story.
Show me
Will every tile match a whole word?
○ Captured trace — raw BPE pieces (Ġ space, Ċ newline). Offsets are Unicode codepoints, sometimes shared; individual decoding can be incomplete.
<|im_start|> · ID 1system · ID 9690Ċ · ID 198You · ID 2683Ġare · ID 359Ġa · ID 253Ġhelpful · ID 5356ĠAI · ID 5646Ġassistant · ID 11173Ġnamed · ID 3365ĠSm · ID 3511ol · ID 308LM · ID 34519, · ID 28Ġtrained · ID 7018Ġby · ID 411ĠH · ID 407ugging · ID 19712ĠFace · ID 8182<|im_end|> · ID 2Ċ · ID 198<|im_start|> · ID 1user · ID 4093Ċ · ID 198Write · ID 19161Ġa · ID 253Ġmessage · ID 3714Ġto · ID 288Ġlet · ID 1303Ġsomeone · ID 2206Ġknow · ID 699ĠI · ID 339Ġwill · ID 523Ġbe · ID 325Ġlate · ID 3008. · ID 30<|im_end|> · ID 2Ċ · ID 198<|im_start|> · ID 1ass · ID 520istant · ID 9531Ċ · ID 198Inspected position 41 · ID 198 · [198, 199) · Single-token decode:
"\n"
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant
Tokenization alone does not understand a sentence. Other tokenizers can segment it differently.
The fragment, its ID and its place in the sequence are different things.
Go deeper
A displayed character can span several tokens, and a token need not decode into a complete Unicode character on its own. Grouped display fragments must list their underlying tokens rather than pretending to be a single token.
Decoding the whole sequence follows this tokenizer's rules. If normalization changes the input, show the original and normalized forms explicitly; do not claim byte-for-byte equality.
S05 · An ID looks up a list of numbers
What can the model calculate with an ID?
An ID selects a row in a learned table: a list of numbers called an embedding. This is a starting representation used in the calculations.
The model also accounts for positions in the sequence. The few values shown here are not a complete map of meaning.
- Piece → IDThe token keeps its identity.
- Learned tableThe ID addresses one row.
- RepresentationA list of numbers for calculation.
Recorded lookup — selected dimensions; layout is not semantic distance.
Show me
Is the token's ID already its full representation?
Display precision: the last digits of numbers may vary after export, without renormalization. Canonical data and SHA256: trace manifest.
Position 41 · Ċ → ID 198 → learned table row. Parameters stay fixed.
□ Documented reduced view of the initial prompt: dimensions 0, 1, 2, 3, 4, 5, 6, 7 / 576. Signed captured values; no human axes or quantitative colour scale.
Position 41 · Ċ → ID 198 → learned table row
| Dimension | Embedding |
|---|---|
| 0 | -0.2119140625 |
| 1 | -0.1201171875 |
| 2 | 0.1728515625 |
| 3 | 0.056396484375 |
| 4 | 0.216796875 |
| 5 | -0.0308837890625 |
| 6 | 0.169921875 |
| 7 | -0.16796875 |
RoPE handles position through rotations in attention, not psychological axes or a universal addition to this table.
The ID finds a learned row; that list of values can take part in subsequent calculations.
Go deeper
A vector is an ordered list of numbers; a dimension is one numbered place in that list. Each displayed cell must name its dimension and extraction point. Use selected dimensions unless an explicitly documented projection is needed; neither option makes the full space three-dimensional.
The way position enters the calculation depends on the chosen architecture. L02 must document it; the drawing must not assume that position is always added to the embedding.
S06 · Context changes the representation
How does the surrounding input affect a token's representation?
The model has successive calculation stages, called layers. Within a layer, attention combines information from allowed positions, never later positions here.
Other learned transformations also change the representation. The original tokens and the learned parameters stay fixed during this reply.
- Allowed sourcesThe inspected position and earlier ones. Never the future.
- One open layerAttention, then other calculations including the MLP.
- Same position, afterA transformed representation, not a new token.
Recorded states and attention — same prefix and position; reduced view, not a complete explanation.
Show me
Can an earlier position read a later token in this model?
Display precision: the last digits of numbers may vary after export, without renormalization. Canonical data and SHA256: trace manifest.
Same inspected position 41 · Ċ · ID 198. Before and after the same layer; text and parameters stay fixed.
Layer 0 · head 0 · zero-based. Block input → RMSNorm, attention, residual, RMSNorm, SiLU MLP, residual → same block output. No new token.
□ Documented reduced view of the initial prompt: dimensions 0, 1, 2, 3, 4, 5, 6, 7 / 576. Signed captured values; no human axes or quantitative colour scale.
Position 41 · Ċ → ID 198 → learned table row
| Dimension | Before | After |
|---|---|---|
| 0 | -0.2119140625 | 0.3470211923122406 |
| 1 | -0.1201171875 | 0.35203033685684204 |
| 2 | 0.1728515625 | 0.6483573317527771 |
| 3 | 0.056396484375 | 1.6338821649551392 |
| 4 | 0.216796875 | 0.10558444261550903 |
| 5 | -0.0308837890625 | 0.1451607346534729 |
| 6 | 0.169921875 | 0.2099938243627548 |
| 7 | -0.16796875 | -0.01370704174041748 |
RoPE handles position through rotations in attention, not psychological axes or a universal addition to this table.
Allowed attention sources — initial prompt
- 0 ·
<|im_start|>· 0.016806183382868767 - 1 ·
system· 0.03623035550117493 - 2 ·
Ċ· 0.013212859630584717 - 3 ·
You· 0.014478638768196106 - 4 ·
Ġare· 0.0367521271109581 - 5 ·
Ġa· 0.036762870848178864 - 6 ·
Ġhelpful· 0.008883410133421421 - 7 ·
ĠAI· 0.020600130781531337 - 8 ·
Ġassistant· 0.016439218074083328 - 9 ·
Ġnamed· 0.008159841410815716 - 10 ·
ĠSm· 0.009223200380802156 - 11 ·
ol· 0.02037954330444336 - 12 ·
LM· 0.006764519028365612 - 13 ·
,· 0.0029480820521712303 - 14 ·
Ġtrained· 0.03501533716917038 - 15 ·
Ġby· 0.001990683376789093 - 16 ·
ĠH· 0.004558082204312086 - 17 ·
ugging· 0.018426261842250824 - 18 ·
ĠFace· 0.018363045528531075 - 19 ·
<|im_end|>· 0.028487153351306915 - 20 ·
Ċ· 0.013511051423847675 - 21 ·
<|im_start|>· 0.048296812921762466 - 22 ·
user· 0.02644071727991104 - 23 ·
Ċ· 0.00906183198094368 - 24 ·
Write· 0.023222768679261208 - 25 ·
Ġa· 0.05316595360636711 - 26 ·
Ġmessage· 0.038014743477106094 - 27 ·
Ġto· 0.0018556704744696615 - 28 ·
Ġlet· 0.02139618434011936 - 29 ·
Ġsomeone· 0.11133842915296556 - 30 ·
Ġknow· 0.025602929294109344 - 31 ·
ĠI· 0.009219696745276453 - 32 ·
Ġwill· 0.028745058923959732 - 33 ·
Ġbe· 0.020472442731261253 - 34 ·
Ġlate· 0.009819515980780125 - 35 ·
.· 0.00329864164814353 - 36 ·
<|im_end|>· 0.051956940442323685 - 37 ·
Ċ· 0.008122499100863934 - 38 ·
<|im_start|>· 0.05343799665570259 - 39 ·
ass· 0.044940754771232605 - 40 ·
istant· 0.03968193382024765 - 41 ·
Ċ· 0.003915850073099136
× Future positions forbidden: no future position in this input.
The entire allowed row is shown, without renormalization. Parameters and IDs stay fixed; computed representations change.
Context contributes to new calculated values, not a rewrite of the original text or a new training step.
Go deeper
A head is one attention calculation within a layer. Display its identity, the selected row, omitted links and weights without renormalizing a hidden subset as if it were complete.
The feed-forward transformation, often called an MLP, also changes the values. Normalization rescales intermediate values; residual connections carry earlier values alongside a transformation. Their order belongs to the architecture-specific explanation. A single attention map is not a complete causal explanation of a response.
S07 · Model scores are not our imposed choice
Who decides the next token in this teaching example?
At the last position of the full input, the model's final representation gives scores for possible next tokens. These scores can be converted to probabilities.
Recorded conditional scores. Imposed choice — not selected by the model. Probability is not truth.
- Final representationAt the last position of the prefix.
- ○ Calculated maximumThe model’s highest score, not the author of the reply.
- ◇ Script targetThe imposed token, even if its score is lower.
Recorded conditional scores. Imposed choice — not selected by the model. Probability is not truth.
Show me
Must the imposed token be the model's highest-scoring token?
Display precision: the last digits of numbers may vary after export, without renormalization. Canonical data and SHA256: trace manifest.
Exact context of this choice
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant
Recorded model calculations — given the displayed prefix · Step prefix 0 · Documented reduced view
Final representation at the last position → logits → full-vocabulary softmax.
Probability ≠ truth. These scores concern continuation of the prefix, not the reliability of a claim.
Model maximum (argmax)
" · ID 18 · rank / rang 1Logit 28.694013595581055 · P 0.2015938808156529 · log(P) -1.601500096342548
The most likely for this prefix. It does not decide which token is appended in our example.
Imposed choice — not selected by the model
Hi · ID 26843 · rank / rang 4Logit 27.388212203979492 · P 0.05462293180677024 · log(P) -2.9073014879441104
Target already in the top 8; its probability is counted only once.
Imposed choice — not selected by the model ·Hi · ID 26843From representation to probabilities
d0: -2.0552053451538086 d1: -0.027857806533575055 d2: 1.2770594358444214 d3: 0.8531920313835144 d4: -0.3432360589504242 d5: -1.2546347379684448 d6: -0.401529848575592 d7: -1.0341060161590576
Top 8 candidates and imposed target. Fixture values displayed by the browser, scientific notation when needed; no renormalization. Full-vocabulary logsumexp: 30.295513691923603
"· ID 18 · rank / rang 1Logit 28.694013595581055 · P 0.2015938808156529 · log(P) -1.601500096342548
I· ID 57 · rank / rang 2Logit 28.587482452392575 · P 0.1812222247894739 · log(P) -1.7080312395310244
Hey· ID 22234 · rank / rang 3Logit 27.419790267944336 · P 0.05637534147455826 · log(P) -2.8757234239792666
Hi· ID 26843 · rank / rang 4Logit 27.388212203979492 · P 0.05462293180677024 · log(P) -2.9073014879441104
Dear· ID 35097 · rank / rang 5Logit 27.24317741394043 · P 0.04724840992530328 · log(P) -3.052336277983173
Hello· ID 19556 · rank / rang 6Logit 27.06204414367676 · P 0.03942048995464733 · log(P) -3.2334695482468447
Sure· ID 34355 · rank / rang 7Logit 26.740507125854492 · P 0.02858118724961435 · log(P) -3.5550065660691104
Please· ID 10180 · rank / rang 8Logit 26.22800064086914 · P 0.01711991195573143 · log(P) -4.067513051054462
Other tokens : 0.3738156220282474 · 49144 omitted candidates.
These tokens are neither in the top 8 nor the imposed target. The target’s probability counts once, even when it appears in both panels. other_probability = mass outside UNION(top8, target).
The maximum breaks ties by lowest ID. The script imposes the target, even when they coincide. Temperature 1, no filters or sampling.
The ordinary loop, unlike our example
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
Inspect ≠ select ≠ append. No token appended yet.
Inspection changes neither the script's imposed choice nor the recorded values.
Go deeper
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
Other mass excludes the union of the top eight candidates and the imposed target. A target outside the top 8 is subtracted once; no softmax over just the visible list.
S08 · Add a token, then repeat
What changes after the first token has been chosen?
In ordinary generation, each chosen token joins the context used at the next step.
Recorded calculations for successive prefixes; tokens imposed by the script. End of prepared path — not a model-selected stop.
- Current prefixThe input and tokens already appended.
- ◇ Imposed choiceThe script fixes the token; its scores are recorded.
- Extended prefixAppending changes the context for the next calculation.
Recorded calculations for successive prefixes; tokens imposed by the script. End of prepared path — not a model-selected stop.
Show me
Can the next step keep using the old context?
Display precision: the last digits of numbers may vary after export, without renormalization. Canonical data and SHA256: trace manifest.
Imposed tokens appended : 16 / 16.
Prefix that produced the last appended token · ID 17
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant Hi, I will be a little late. Sorry to keep you waiting
Final prefix — no next choice captured · 42 + 16 tokens
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant Hi, I will be a little late. Sorry to keep you waiting!
1, 9690, 198, 2683, 359, 253, 5356, 5646, 11173, 3365, 3511, 308, 34519, 28, 7018, 411, 407, 19712, 8182, 2, 198, 1, 4093, 198, 19161, 253, 3714, 288, 1303, 2206, 699, 339, 523, 325, 3008, 30, 2, 198, 1, 520, 9531, 198, 26843, 28, 339, 523, 325, 253, 1838, 3008, 30, 25442, 541, 288, 1446, 346, 7003, 17
End of prepared path — not a model-selected stop: prepared_sequence_end · 16 tokens. No EOS appended, no invented terminal distribution.
Exact ledger of every step
g=0 → 1 · ID 26843
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant
1, 9690, 198, 2683, 359, 253, 5356, 5646, 11173, 3365, 3511, 308, 34519, 28, 7018, 411, 407, 19712, 8182, 2, 198, 1, 4093, 198, 19161, 253, 3714, 288, 1303, 2206, 699, 339, 523, 325, 3008, 30, 2, 198, 1, 520, 9531, 198
Recorded model calculations — given the displayed prefix · Step prefix 0 · Documented reduced view
Final representation at the last position → logits → full-vocabulary softmax.
Probability ≠ truth. These scores concern continuation of the prefix, not the reliability of a claim.
Model maximum (argmax)
" · ID 18 · rank / rang 1Logit 28.694013595581055 · P 0.2015938808156529 · log(P) -1.601500096342548
The most likely for this prefix. It does not decide which token is appended in our example.
Imposed choice — not selected by the model
Hi · ID 26843 · rank / rang 4Logit 27.388212203979492 · P 0.05462293180677024 · log(P) -2.9073014879441104
Target already in the top 8; its probability is counted only once.
Imposed choice — not selected by the model ·Hi · ID 26843From representation to probabilities
d0: -2.0552053451538086 d1: -0.027857806533575055 d2: 1.2770594358444214 d3: 0.8531920313835144 d4: -0.3432360589504242 d5: -1.2546347379684448 d6: -0.401529848575592 d7: -1.0341060161590576
Top 8 candidates and imposed target. Fixture values displayed by the browser, scientific notation when needed; no renormalization. Full-vocabulary logsumexp: 30.295513691923603
"· ID 18 · rank / rang 1Logit 28.694013595581055 · P 0.2015938808156529 · log(P) -1.601500096342548
I· ID 57 · rank / rang 2Logit 28.587482452392575 · P 0.1812222247894739 · log(P) -1.7080312395310244
Hey· ID 22234 · rank / rang 3Logit 27.419790267944336 · P 0.05637534147455826 · log(P) -2.8757234239792666
Hi· ID 26843 · rank / rang 4Logit 27.388212203979492 · P 0.05462293180677024 · log(P) -2.9073014879441104
Dear· ID 35097 · rank / rang 5Logit 27.24317741394043 · P 0.04724840992530328 · log(P) -3.052336277983173
Hello· ID 19556 · rank / rang 6Logit 27.06204414367676 · P 0.03942048995464733 · log(P) -3.2334695482468447
Sure· ID 34355 · rank / rang 7Logit 26.740507125854492 · P 0.02858118724961435 · log(P) -3.5550065660691104
Please· ID 10180 · rank / rang 8Logit 26.22800064086914 · P 0.01711991195573143 · log(P) -4.067513051054462
Other tokens : 0.3738156220282474 · 49144 omitted candidates.
These tokens are neither in the top 8 nor the imposed target. The target’s probability counts once, even when it appears in both panels. other_probability = mass outside UNION(top8, target).
The maximum breaks ties by lowest ID. The script imposes the target, even when they coincide. Temperature 1, no filters or sampling.
The ordinary loop, unlike our example
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
Hi
g=1 → 2 · ID 28
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant Hi
1, 9690, 198, 2683, 359, 253, 5356, 5646, 11173, 3365, 3511, 308, 34519, 28, 7018, 411, 407, 19712, 8182, 2, 198, 1, 4093, 198, 19161, 253, 3714, 288, 1303, 2206, 699, 339, 523, 325, 3008, 30, 2, 198, 1, 520, 9531, 198, 26843
Recorded model calculations — given the displayed prefix · Step prefix 1 · Documented reduced view
Final representation at the last position → logits → full-vocabulary softmax.
Probability ≠ truth. These scores concern continuation of the prefix, not the reliability of a claim.
Model maximum (argmax)
Ġ[ · ID 933 · rank / rang 1Logit 7.943227291107178 · P 0.7508391956959775 · log(P) -0.28656377039005143
The most likely for this prefix. It does not decide which token is appended in our example.
Imposed choice — not selected by the model
, · ID 28 · rank / rang 6Logit 3.8363325595855713 · P 0.012357915353324738 · log(P) -4.393458501911658
Target already in the top 8; its probability is counted only once.
Imposed choice — not selected by the model ·, · ID 28From representation to probabilities
d0: -1.3738007545471191 d1: -0.5202157497406006 d2: 1.876486659049988 d3: 0.9552001953125 d4: -0.08893979340791702 d5: -2.5445735454559326 d6: -1.5463964939117432 d7: -1.7778035402297974
Top 8 candidates and imposed target. Fixture values displayed by the browser, scientific notation when needed; no renormalization. Full-vocabulary logsumexp: 8.22979106149723
Ġ[· ID 933 · rank / rang 1Logit 7.943227291107178 · P 0.7508391956959775 · log(P) -0.28656377039005143
ĠAlex· ID 5325 · rank / rang 2Logit 4.963054656982422 · P 0.038130667303635395 · log(P) -3.2667364045148073
ĠEmily· ID 15487 · rank / rang 3Logit 4.271503448486328 · P 0.0190957856855478 · log(P) -3.958287613010901
ĠJohn· ID 2322 · rank / rang 4Logit 4.26744270324707 · P 0.01901839979327099 · log(P) -3.962348358250159
ĠSarah· ID 8165 · rank / rang 5Logit 3.86134672164917 · P 0.01267093691526863 · log(P) -4.368444339848059
,· ID 28 · rank / rang 6Logit 3.8363325595855713 · P 0.012357915353324738 · log(P) -4.393458501911658
Ġthere· ID 665 · rank / rang 7Logit 3.5609374046325684 · P 0.009383019524256448 · log(P) -4.668853656864661
ĠLi· ID 9679 · rank / rang 8Logit 3.2445428371429443 · P 0.006838080438675774 · log(P) -4.985248224354285
Other tokens : 0.1316659992900421 · 49144 omitted candidates.
These tokens are neither in the top 8 nor the imposed target. The target’s probability counts once, even when it appears in both panels. other_probability = mass outside UNION(top8, target).
The maximum breaks ties by lowest ID. The script imposes the target, even when they coincide. Temperature 1, no filters or sampling.
The ordinary loop, unlike our example
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
Hi,
g=2 → 3 · ID 339
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant Hi,
1, 9690, 198, 2683, 359, 253, 5356, 5646, 11173, 3365, 3511, 308, 34519, 28, 7018, 411, 407, 19712, 8182, 2, 198, 1, 4093, 198, 19161, 253, 3714, 288, 1303, 2206, 699, 339, 523, 325, 3008, 30, 2, 198, 1, 520, 9531, 198, 26843, 28
Recorded model calculations — given the displayed prefix · Step prefix 2 · Documented reduced view
Final representation at the last position → logits → full-vocabulary softmax.
Probability ≠ truth. These scores concern continuation of the prefix, not the reliability of a claim.
Model maximum (argmax)
ĠI · ID 339 · rank / rang 1Logit 12.261602401733398 · P 0.5416538339823407 · log(P) -0.6131281642765725
The most likely for this prefix. It does not decide which token is appended in our example.
Imposed choice — not selected by the model
ĠI · ID 339 · rank / rang 1Logit 12.261602401733398 · P 0.5416538339823407 · log(P) -0.6131281642765725
Target already in the top 8; its probability is counted only once.
Imposed choice — not selected by the model ·ĠI · ID 339From representation to probabilities
d0: -1.3890047073364258 d1: 0.04630286619067192 d2: 1.4595191478729248 d3: 0.8313894271850586 d4: 0.3924960494041443 d5: -1.2829383611679075 d6: -0.03668787330389023 d7: -3.1501736640930176
Top 8 candidates and imposed target. Fixture values displayed by the browser, scientific notation when needed; no renormalization. Full-vocabulary logsumexp: 12.874730566009973
ĠI· ID 339 · rank / rang 1Logit 12.261602401733398 · P 0.5416538339823407 · log(P) -0.6131281642765725
Ġit· ID 357 · rank / rang 2Logit 10.191718101501465 · P 0.06835692087697401 · log(P) -2.683012464508506
Ġthanks· ID 6954 · rank / rang 3Logit 10.008417129516602 · P 0.056908336537826 · log(P) -2.8663134364933693
Ġhow· ID 638 · rank / rang 4Logit 9.530943870544434 · P 0.03530302250047117 · log(P) -3.3437866954655373
Ċ· ID 198 · rank / rang 5Logit 8.772052764892578 · P 0.01652835643822013 · log(P) -4.102677801117393
Ġsorry· ID 22657 · rank / rang 6Logit 8.762948036193848 · P 0.016378553230636035 · log(P) -4.111782529816123
Ġcan· ID 416 · rank / rang 7Logit 8.560697555541992 · P 0.01337948105089643 · log(P) -4.314033010467979
Ġthank· ID 9984 · rank / rang 8Logit 8.480876922607422 · P 0.012353033192863324 · log(P) -4.393853643402549
Other tokens : 0.23913846218977125 · 49144 omitted candidates.
These tokens are neither in the top 8 nor the imposed target. The target’s probability counts once, even when it appears in both panels. other_probability = mass outside UNION(top8, target).
The maximum breaks ties by lowest ID. The script imposes the target, even when they coincide. Temperature 1, no filters or sampling.
The ordinary loop, unlike our example
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
Hi, I
g=3 → 4 · ID 523
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant Hi, I
1, 9690, 198, 2683, 359, 253, 5356, 5646, 11173, 3365, 3511, 308, 34519, 28, 7018, 411, 407, 19712, 8182, 2, 198, 1, 4093, 198, 19161, 253, 3714, 288, 1303, 2206, 699, 339, 523, 325, 3008, 30, 2, 198, 1, 520, 9531, 198, 26843, 28, 339
Recorded model calculations — given the displayed prefix · Step prefix 3 · Documented reduced view
Final representation at the last position → logits → full-vocabulary softmax.
Probability ≠ truth. These scores concern continuation of the prefix, not the reliability of a claim.
Model maximum (argmax)
'm · ID 5248 · rank / rang 1Logit 29.538902282714844 · P 0.6647063412775203 · log(P) -0.4084099279206512
The most likely for this prefix. It does not decide which token is appended in our example.
Imposed choice — not selected by the model
Ġwill · ID 523 · rank / rang 11Logit 25.369293212890625 · P 0.010275231404390506 · log(P) -4.57801899774487
Target outside top 8 — not promoted in the ranking.
Imposed choice — not selected by the model ·Ġwill · ID 523From representation to probabilities
d0: 1.0284905433654783 d1: -0.5418347716331482 d2: -0.4579586684703827 d3: 0.5164719820022583 d4: 1.357612371444702 d5: -1.0663337707519531 d6: 2.732614517211914 d7: -2.6122002601623535
Top 8 candidates and imposed target. Fixture values displayed by the browser, scientific notation when needed; no renormalization. Full-vocabulary logsumexp: 29.947312210635495
'm· ID 5248 · rank / rang 1Logit 29.538902282714844 · P 0.6647063412775203 · log(P) -0.4084099279206512
Ġhope· ID 3826 · rank / rang 2Logit 27.380939483642575 · P 0.07681366484450634 · log(P) -2.566372726992917
Ġam· ID 744 · rank / rang 3Logit 26.44110870361328 · P 0.03001063359304285 · log(P) -3.5062035070222137
'll· ID 3060 · rank / rang 4Logit 26.440147399902344 · P 0.029981798121645684 · log(P) -3.5071648107331512
Ġwas· ID 436 · rank / rang 5Logit 26.164474487304688 · P 0.022758018746148117 · log(P) -3.782837723330807
've· ID 3543 · rank / rang 6Logit 26.095613479614254 · P 0.02124361857591764 · log(P) -3.851698731021237
Ġjust· ID 915 · rank / rang 7Logit 25.748607635498047 · P 0.015015015051184615 · log(P) -4.198704575137448
Ġwanted· ID 4146 · rank / rang 8Logit 25.705127716064453 · P 0.01437615288856432 · log(P) -4.242184494571042
Other tokens : 0.11481952549707986 · 49143 omitted candidates.
These tokens are neither in the top 8 nor the imposed target. The target’s probability counts once, even when it appears in both panels. other_probability = mass outside UNION(top8, target).
The maximum breaks ties by lowest ID. The script imposes the target, even when they coincide. Temperature 1, no filters or sampling.
The ordinary loop, unlike our example
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
Hi, I will
g=4 → 5 · ID 325
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant Hi, I will
1, 9690, 198, 2683, 359, 253, 5356, 5646, 11173, 3365, 3511, 308, 34519, 28, 7018, 411, 407, 19712, 8182, 2, 198, 1, 4093, 198, 19161, 253, 3714, 288, 1303, 2206, 699, 339, 523, 325, 3008, 30, 2, 198, 1, 520, 9531, 198, 26843, 28, 339, 523
Recorded model calculations — given the displayed prefix · Step prefix 4 · Documented reduced view
Final representation at the last position → logits → full-vocabulary softmax.
Probability ≠ truth. These scores concern continuation of the prefix, not the reliability of a claim.
Model maximum (argmax)
Ġbe · ID 325 · rank / rang 1Logit 27.738601684570312 · P 0.9486104014679388 · log(P) -0.05275710052609739
The most likely for this prefix. It does not decide which token is appended in our example.
Imposed choice — not selected by the model
Ġbe · ID 325 · rank / rang 1Logit 27.738601684570312 · P 0.9486104014679388 · log(P) -0.05275710052609739
Target already in the top 8; its probability is counted only once.
Imposed choice — not selected by the model ·Ġbe · ID 325From representation to probabilities
d0: -0.7276690602302551 d1: -2.4926223754882812 d2: -0.4160797894001007 d3: -0.05029306933283806 d4: 0.969337522983551 d5: -1.3014726638793943 d6: 0.22555148601531985 d7: -2.047252893447876
Top 8 candidates and imposed target. Fixture values displayed by the browser, scientific notation when needed; no renormalization. Full-vocabulary logsumexp: 27.79135878509641
Ġbe· ID 325 · rank / rang 1Logit 27.738601684570312 · P 0.9486104014679388 · log(P) -0.05275710052609739
Ġneed· ID 737 · rank / rang 2Logit 23.395551681518555 · P 0.012328925588765326 · log(P) -4.395807103577855
Ġhave· ID 457 · rank / rang 3Logit 23.09413719177246 · P 0.009120582618545262 · log(P) -4.697221593323949
Ġmiss· ID 3956 · rank / rang 4Logit 22.54572296142578 · P 0.005270469539751897 · log(P) -5.245635823670629
Ġnot· ID 441 · rank / rang 5Logit 22.185312271118164 · P 0.0036755719933553375 · log(P) -5.606046513978246
Ġprobably· ID 3105 · rank / rang 6Logit 21.73445701599121 · P 0.002341644616057088 · log(P) -6.056901769105199
Ġtry· ID 1576 · rank / rang 7Logit 21.62902069091797 · P 0.002107320393668632 · log(P) -6.162338094178441
Ġget· ID 820 · rank / rang 8Logit 21.475494384765625 · P 0.0018074027656234824 · log(P) -6.315864400330785
Other tokens : 0.014737681016295376 · 49144 omitted candidates.
These tokens are neither in the top 8 nor the imposed target. The target’s probability counts once, even when it appears in both panels. other_probability = mass outside UNION(top8, target).
The maximum breaks ties by lowest ID. The script imposes the target, even when they coincide. Temperature 1, no filters or sampling.
The ordinary loop, unlike our example
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
Hi, I will be
g=5 → 6 · ID 253
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant Hi, I will be
1, 9690, 198, 2683, 359, 253, 5356, 5646, 11173, 3365, 3511, 308, 34519, 28, 7018, 411, 407, 19712, 8182, 2, 198, 1, 4093, 198, 19161, 253, 3714, 288, 1303, 2206, 699, 339, 523, 325, 3008, 30, 2, 198, 1, 520, 9531, 198, 26843, 28, 339, 523, 325
Recorded model calculations — given the displayed prefix · Step prefix 5 · Documented reduced view
Final representation at the last position → logits → full-vocabulary softmax.
Probability ≠ truth. These scores concern continuation of the prefix, not the reliability of a claim.
Model maximum (argmax)
Ġarriving · ID 18563 · rank / rang 1Logit 22.357425689697266 · P 0.49350416399246727 · log(P) -0.706223982469492
The most likely for this prefix. It does not decide which token is appended in our example.
Imposed choice — not selected by the model
Ġa · ID 253 · rank / rang 17Logit 17.270858764648438 · P 0.003049459529467995 · log(P) -5.79279090751832
Target outside top 8 — not promoted in the ranking.
Imposed choice — not selected by the model ·Ġa · ID 253From representation to probabilities
d0: -0.6928384304046631 d1: -0.9491083025932312 d2: -0.5656501650810242 d3: 0.4913584887981415 d4: 1.20679771900177 d5: -0.6256847977638245 d6: 0.007863008417189121 d7: -2.1528191566467285
Top 8 candidates and imposed target. Fixture values displayed by the browser, scientific notation when needed; no renormalization. Full-vocabulary logsumexp: 23.06364967216676
Ġarriving· ID 18563 · rank / rang 1Logit 22.357425689697266 · P 0.49350416399246727 · log(P) -0.706223982469492
Ġlate· ID 3008 · rank / rang 2Logit 21.149677276611328 · P 0.14749331952919614 · log(P) -1.9139723955554295
Ġat· ID 418 · rank / rang 3Logit 20.44598960876465 · P 0.07297341637502132 · log(P) -2.617660063402109
Ġin· ID 281 · rank / rang 4Logit 19.95261001586914 · P 0.044554609772080714 · log(P) -3.111039656297617
Ġon· ID 335 · rank / rang 5Logit 19.290359497070312 · P 0.02297634259814696 · log(P) -3.773290175096445
Ġgoing· ID 2045 · rank / rang 6Logit 18.950429916381836 · P 0.016355030456421685 · log(P) -4.113219755784922
Ġcoming· ID 4167 · rank / rang 7Logit 18.836090087890625 · P 0.014587947800722006 · log(P) -4.227559584276133
Ġhere· ID 1535 · rank / rang 8Logit 18.456806182861328 · P 0.00998328095874125 · log(P) -4.6068434893054295
Other tokens : 0.17452242898773507 · 49143 omitted candidates.
These tokens are neither in the top 8 nor the imposed target. The target’s probability counts once, even when it appears in both panels. other_probability = mass outside UNION(top8, target).
The maximum breaks ties by lowest ID. The script imposes the target, even when they coincide. Temperature 1, no filters or sampling.
The ordinary loop, unlike our example
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
Hi, I will be a
g=6 → 7 · ID 1838
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant Hi, I will be a
1, 9690, 198, 2683, 359, 253, 5356, 5646, 11173, 3365, 3511, 308, 34519, 28, 7018, 411, 407, 19712, 8182, 2, 198, 1, 4093, 198, 19161, 253, 3714, 288, 1303, 2206, 699, 339, 523, 325, 3008, 30, 2, 198, 1, 520, 9531, 198, 26843, 28, 339, 523, 325, 253
Recorded model calculations — given the displayed prefix · Step prefix 6 · Documented reduced view
Final representation at the last position → logits → full-vocabulary softmax.
Probability ≠ truth. These scores concern continuation of the prefix, not the reliability of a claim.
Model maximum (argmax)
Ġbit · ID 3241 · rank / rang 1Logit 24.410703659057617 · P 0.3844536556696628 · log(P) -0.9559320287192355
The most likely for this prefix. It does not decide which token is appended in our example.
Imposed choice — not selected by the model
Ġlittle · ID 1838 · rank / rang 2Logit 24.38994407653809 · P 0.3765548301116357 · log(P) -0.9766916112387668
Target already in the top 8; its probability is counted only once.
Imposed choice — not selected by the model ·Ġlittle · ID 1838From representation to probabilities
d0: -2.269467353820801 d1: -0.06955106556415558 d2: -0.49502983689308167 d3: 1.1438674926757812 d4: 0.37673303484916687 d5: -0.2138453871011734 d6: -2.409039258956909 d7: -2.5745697021484375
Top 8 candidates and imposed target. Fixture values displayed by the browser, scientific notation when needed; no renormalization. Full-vocabulary logsumexp: 25.366635687776853
Ġbit· ID 3241 · rank / rang 1Logit 24.410703659057617 · P 0.3844536556696628 · log(P) -0.9559320287192355
Ġlittle· ID 1838 · rank / rang 2Logit 24.38994407653809 · P 0.3765548301116357 · log(P) -0.9766916112387668
Ġlot· ID 2341 · rank / rang 3Logit 22.784648895263672 · P 0.07562360625301334 · log(P) -2.581986792513181
Ġlate· ID 3008 · rank / rang 4Logit 22.3887939453125 · P 0.05090257626492618 · log(P) -2.977841742464353
Ġfew· ID 1443 · rank / rang 5Logit 20.868349075317383 · P 0.011128046870138878 · log(P) -4.49828661245947
-· ID 29 · rank / rang 6Logit 20.341785430908203 · P 0.006572570618550343 · log(P) -5.02485025686865
arr· ID 2110 · rank / rang 7Logit 19.67495346069336 · P 0.0033739125058068658 · log(P) -5.6916822270834935
Ġday· ID 1194 · rank / rang 8Logit 19.67462921142578 · P 0.003372818694491328 · log(P) -5.692006476351072
Other tokens : 0.08801798301177345 · 49144 omitted candidates.
These tokens are neither in the top 8 nor the imposed target. The target’s probability counts once, even when it appears in both panels. other_probability = mass outside UNION(top8, target).
The maximum breaks ties by lowest ID. The script imposes the target, even when they coincide. Temperature 1, no filters or sampling.
The ordinary loop, unlike our example
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
Hi, I will be a little
g=7 → 8 · ID 3008
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant Hi, I will be a little
1, 9690, 198, 2683, 359, 253, 5356, 5646, 11173, 3365, 3511, 308, 34519, 28, 7018, 411, 407, 19712, 8182, 2, 198, 1, 4093, 198, 19161, 253, 3714, 288, 1303, 2206, 699, 339, 523, 325, 3008, 30, 2, 198, 1, 520, 9531, 198, 26843, 28, 339, 523, 325, 253, 1838
Recorded model calculations — given the displayed prefix · Step prefix 7 · Documented reduced view
Final representation at the last position → logits → full-vocabulary softmax.
Probability ≠ truth. These scores concern continuation of the prefix, not the reliability of a claim.
Model maximum (argmax)
Ġbit · ID 3241 · rank / rang 1Logit 24.832923889160156 · P 0.377625762409583 · log(P) -0.9738516203178342
The most likely for this prefix. It does not decide which token is appended in our example.
Imposed choice — not selected by the model
Ġlate · ID 3008 · rank / rang 2Logit 24.48090171813965 · P 0.2655708042078022 · log(P) -1.325873791338342
Target already in the top 8; its probability is counted only once.
Imposed choice — not selected by the model ·Ġlate · ID 3008From representation to probabilities
d0: -2.0251805782318115 d1: -1.4032589197158811 d2: 0.05142971500754357 d3: 1.056714415550232 d4: 0.015530036762356758 d5: 0.029730547219514847 d6: 0.8244141340255737 d7: -1.4314391613006592
Top 8 candidates and imposed target. Fixture values displayed by the browser, scientific notation when needed; no renormalization. Full-vocabulary logsumexp: 25.80677550947799
Ġbit· ID 3241 · rank / rang 1Logit 24.832923889160156 · P 0.377625762409583 · log(P) -0.9738516203178342
Ġlate· ID 3008 · rank / rang 2Logit 24.48090171813965 · P 0.2655708042078022 · log(P) -1.325873791338342
Ġearly· ID 1584 · rank / rang 3Logit 23.934722900390625 · P 0.15380763064149974 · log(P) -1.8720526090873657
Ġearlier· ID 3685 · rank / rang 4Logit 22.39000701904297 · P 0.03281831647187173 · log(P) -3.4167684904350217
Ġlater· ID 1916 · rank / rang 5Logit 22.140840530395508 · P 0.025580243107053553 · log(P) -3.6659349790824822
Ġbefore· ID 1092 · rank / rang 6Logit 21.63579559326172 · P 0.01543712562011995 · log(P) -4.170979916216272
Ġover· ID 690 · rank / rang 7Logit 21.36518096923828 · P 0.011777144409081643 · log(P) -4.441594540239709
Ġmore· ID 540 · rank / rang 8Logit 21.344587326049805 · P 0.011537090376698696 · log(P) -4.462188183428186
Other tokens : 0.10584588275629098 · 49144 omitted candidates.
These tokens are neither in the top 8 nor the imposed target. The target’s probability counts once, even when it appears in both panels. other_probability = mass outside UNION(top8, target).
The maximum breaks ties by lowest ID. The script imposes the target, even when they coincide. Temperature 1, no filters or sampling.
The ordinary loop, unlike our example
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
Hi, I will be a little late
g=8 → 9 · ID 30
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant Hi, I will be a little late
1, 9690, 198, 2683, 359, 253, 5356, 5646, 11173, 3365, 3511, 308, 34519, 28, 7018, 411, 407, 19712, 8182, 2, 198, 1, 4093, 198, 19161, 253, 3714, 288, 1303, 2206, 699, 339, 523, 325, 3008, 30, 2, 198, 1, 520, 9531, 198, 26843, 28, 339, 523, 325, 253, 1838, 3008
Recorded model calculations — given the displayed prefix · Step prefix 8 · Documented reduced view
Final representation at the last position → logits → full-vocabulary softmax.
Probability ≠ truth. These scores concern continuation of the prefix, not the reliability of a claim.
Model maximum (argmax)
. · ID 30 · rank / rang 1Logit 19.96644592285156 · P 0.314481590195263 · log(P) -1.156829741295006
The most likely for this prefix. It does not decide which token is appended in our example.
Imposed choice — not selected by the model
. · ID 30 · rank / rang 1Logit 19.96644592285156 · P 0.314481590195263 · log(P) -1.156829741295006
Target already in the top 8; its probability is counted only once.
Imposed choice — not selected by the model ·. · ID 30From representation to probabilities
d0: -1.2886192798614502 d1: -0.30560430884361267 d2: -1.0620988607406616 d3: 1.7276201248168943 d4: 0.29113897681236267 d5: -1.7176393270492554 d6: -1.6500927209854126 d7: -2.166079044342041
Top 8 candidates and imposed target. Fixture values displayed by the browser, scientific notation when needed; no renormalization. Full-vocabulary logsumexp: 21.12327566414657
.· ID 30 · rank / rang 1Logit 19.96644592285156 · P 0.314481590195263 · log(P) -1.156829741295006
Ġfor· ID 327 · rank / rang 2Logit 18.985572814941406 · P 0.11792542460230356 · log(P) -2.137702849205162
Ġto· ID 288 · rank / rang 3Logit 18.731313705444336 · P 0.09145008646080596 · log(P) -2.3919619587022325
Ġand· ID 284 · rank / rang 4Logit 18.48525047302246 · P 0.0715023336663849 · log(P) -2.6380251911241075
,· ID 28 · rank / rang 5Logit 18.44091796875 · P 0.06840169353362814 · log(P) -2.6823576953965684
Ġtoday· ID 1834 · rank / rang 6Logit 17.808347702026367 · P 0.03633666609944325 · log(P) -3.3149279621202012
Ġthis· ID 451 · rank / rang 7Logit 17.516998291015625 · P 0.02715273847274255 · log(P) -3.606277373130943
Ġtonight· ID 34451 · rank / rang 8Logit 17.304285049438477 · P 0.021949945668392624 · log(P) -3.818990614708092
Other tokens : 0.2507995213010349 · 49144 omitted candidates.
These tokens are neither in the top 8 nor the imposed target. The target’s probability counts once, even when it appears in both panels. other_probability = mass outside UNION(top8, target).
The maximum breaks ties by lowest ID. The script imposes the target, even when they coincide. Temperature 1, no filters or sampling.
The ordinary loop, unlike our example
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
Hi, I will be a little late.
g=9 → 10 · ID 25442
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant Hi, I will be a little late.
1, 9690, 198, 2683, 359, 253, 5356, 5646, 11173, 3365, 3511, 308, 34519, 28, 7018, 411, 407, 19712, 8182, 2, 198, 1, 4093, 198, 19161, 253, 3714, 288, 1303, 2206, 699, 339, 523, 325, 3008, 30, 2, 198, 1, 520, 9531, 198, 26843, 28, 339, 523, 325, 253, 1838, 3008, 30
Recorded model calculations — given the displayed prefix · Step prefix 9 · Documented reduced view
Final representation at the last position → logits → full-vocabulary softmax.
Probability ≠ truth. These scores concern continuation of the prefix, not the reliability of a claim.
Model maximum (argmax)
ĠI · ID 339 · rank / rang 1Logit 15.978963851928713 · P 0.24033545664473255 · log(P) -1.425719595544134
The most likely for this prefix. It does not decide which token is appended in our example.
Imposed choice — not selected by the model
ĠSor · ID 25442 · rank / rang 8Logit 13.81415557861328 · P 0.02758376596844217 · log(P) -3.5905278688595637
Target already in the top 8; its probability is counted only once.
Imposed choice — not selected by the model ·ĠSor · ID 25442From representation to probabilities
d0: -1.1852322816848757 d1: -0.6666378974914551 d2: 0.8172832131385803 d3: 1.3176355361938477 d4: 0.6936988234519958 d5: -2.0851871967315674 d6: -0.9130510687828064 d7: -1.7433034181594849
Top 8 candidates and imposed target. Fixture values displayed by the browser, scientific notation when needed; no renormalization. Full-vocabulary logsumexp: 17.404683447472845
ĠI· ID 339 · rank / rang 1Logit 15.978963851928713 · P 0.24033545664473255 · log(P) -1.425719595544134
ĠCan· ID 1978 · rank / rang 2Logit 15.119123458862305 · P 0.10171708574520136 · log(P) -2.28555998861054
ĠPlease· ID 8765 · rank / rang 3Logit 15.051177024841309 · P 0.09503534322406494 · log(P) -2.3535064226315363
<|im_end|>· ID 2 · rank / rang 4Logit 14.410785675048828 · P 0.05009180924283071 · log(P) -2.993897772424017
ĠIt· ID 657 · rank / rang 5Logit 14.340883255004885 · P 0.04670985108876475 · log(P) -3.063800192467962
ĠHow· ID 1073 · rank / rang 6Logit 14.08203125 · P 0.03605707434494237 · log(P) -3.322652197472845
ĠYou· ID 1206 · rank / rang 7Logit 13.96531105041504 · P 0.032084815534940914 · log(P) -3.439372397057806
ĠSor· ID 25442 · rank / rang 8Logit 13.81415557861328 · P 0.02758376596844217 · log(P) -3.5905278688595637
Other tokens : 0.3703847982060817 · 49144 omitted candidates.
These tokens are neither in the top 8 nor the imposed target. The target’s probability counts once, even when it appears in both panels. other_probability = mass outside UNION(top8, target).
The maximum breaks ties by lowest ID. The script imposes the target, even when they coincide. Temperature 1, no filters or sampling.
The ordinary loop, unlike our example
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
Hi, I will be a little late. Sor
g=10 → 11 · ID 541
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant Hi, I will be a little late. Sor
1, 9690, 198, 2683, 359, 253, 5356, 5646, 11173, 3365, 3511, 308, 34519, 28, 7018, 411, 407, 19712, 8182, 2, 198, 1, 4093, 198, 19161, 253, 3714, 288, 1303, 2206, 699, 339, 523, 325, 3008, 30, 2, 198, 1, 520, 9531, 198, 26843, 28, 339, 523, 325, 253, 1838, 3008, 30, 25442
Recorded model calculations — given the displayed prefix · Step prefix 10 · Documented reduced view
Final representation at the last position → logits → full-vocabulary softmax.
Probability ≠ truth. These scores concern continuation of the prefix, not the reliability of a claim.
Model maximum (argmax)
ry · ID 541 · rank / rang 1Logit 33.729183197021484 · P 0.9999608664391816 · log(P) -0.00003913432655622273
The most likely for this prefix. It does not decide which token is appended in our example.
Imposed choice — not selected by the model
ry · ID 541 · rank / rang 1Logit 33.729183197021484 · P 0.9999608664391816 · log(P) -0.00003913432655622273
Target already in the top 8; its probability is counted only once.
Imposed choice — not selected by the model ·ry · ID 541From representation to probabilities
d0: -2.991468667984009 d1: -0.8253179788589478 d2: 2.7496345043182373 d3: 1.4738621711730957 d4: -2.201028823852539 d5: -0.8349835276603699 d6: 1.1787775754928589 d7: -0.9351224899291992
Top 8 candidates and imposed target. Fixture values displayed by the browser, scientific notation when needed; no renormalization. Full-vocabulary logsumexp: 33.72922233134804
ry· ID 541 · rank / rang 1Logit 33.729183197021484 · P 0.9999608664391816 · log(P) -0.00003913432655622273
r· ID 98 · rank / rang 2Logit 23.45769500732422 · P 0.000034604482785306724 · log(P) -10.271527324023822
ries· ID 1333 · rank / rang 3Logit 20.316591262817383 · P 0.0000014961265817374285 · log(P) -13.412631068530658
os· ID 395 · rank / rang 4Logit 19.061750411987305 · P 4.265774383831879e-7 · log(P) -14.667471919360736
rell· ID 12159 · rank / rang 5Logit 18.704822540283203 · P 2.985286911238753e-7 · log(P) -15.024399791064836
rent· ID 1160 · rank / rang 6Logit 18.517757415771484 · P 2.475966278494743e-7 · log(P) -15.211464915576556
rying· ID 11089 · rank / rang 7Logit 18.411640167236328 · P 2.226684007938849e-7 · log(P) -15.317582164111712
ried· ID 2041 · rank / rang 8Logit 18.383697509765625 · P 2.1653257875596153e-7 · log(P) -15.345524821582416
Other tokens : 0.0000016210477123238883 · 49144 omitted candidates.
These tokens are neither in the top 8 nor the imposed target. The target’s probability counts once, even when it appears in both panels. other_probability = mass outside UNION(top8, target).
The maximum breaks ties by lowest ID. The script imposes the target, even when they coincide. Temperature 1, no filters or sampling.
The ordinary loop, unlike our example
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
Hi, I will be a little late. Sorry
g=11 → 12 · ID 288
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant Hi, I will be a little late. Sorry
1, 9690, 198, 2683, 359, 253, 5356, 5646, 11173, 3365, 3511, 308, 34519, 28, 7018, 411, 407, 19712, 8182, 2, 198, 1, 4093, 198, 19161, 253, 3714, 288, 1303, 2206, 699, 339, 523, 325, 3008, 30, 2, 198, 1, 520, 9531, 198, 26843, 28, 339, 523, 325, 253, 1838, 3008, 30, 25442, 541
Recorded model calculations — given the displayed prefix · Step prefix 11 · Documented reduced view
Final representation at the last position → logits → full-vocabulary softmax.
Probability ≠ truth. These scores concern continuation of the prefix, not the reliability of a claim.
Model maximum (argmax)
Ġfor · ID 327 · rank / rang 1Logit 17.719886779785156 · P 0.4803608658735202 · log(P) -0.7332176536400254
The most likely for this prefix. It does not decide which token is appended in our example.
Imposed choice — not selected by the model
Ġto · ID 288 · rank / rang 4Logit 15.189250946044922 · P 0.038240753466153105 · log(P) -3.26385348738026
Target already in the top 8; its probability is counted only once.
Imposed choice — not selected by the model ·Ġto · ID 288From representation to probabilities
d0: 0.29135775566101074 d1: -2.2683322429656982 d2: 0.8311927318572998 d3: 1.8787803649902344 d4: 1.275809407234192 d5: -2.0554420948028564 d6: -1.340942621231079 d7: -1.1240665912628174
Top 8 candidates and imposed target. Fixture values displayed by the browser, scientific notation when needed; no renormalization. Full-vocabulary logsumexp: 18.45310443342518
Ġfor· ID 327 · rank / rang 1Logit 17.719886779785156 · P 0.4803608658735202 · log(P) -0.7332176536400254
Ġabout· ID 563 · rank / rang 2Logit 17.004520416259766 · P 0.2349026708638545 · log(P) -1.448584017165416
,· ID 28 · rank / rang 3Logit 16.46103286743164 · P 0.13641254495466326 · log(P) -1.992071565993541
Ġto· ID 288 · rank / rang 4Logit 15.189250946044922 · P 0.038240753466153105 · log(P) -3.26385348738026
!· ID 17 · rank / rang 5Logit 14.969992637634276 · P 0.03071169366446069 · log(P) -3.4831117957909044
.· ID 30 · rank / rang 6Logit 14.595712661743164 · P 0.02112302139961681 · log(P) -3.8573917716820176
ĠI· ID 339 · rank / rang 7Logit 14.11510944366455 · P 0.013062692797345517 · log(P) -4.337994989760631
Ġif· ID 585 · rank / rang 8Logit 13.74203109741211 · P 0.008995117615490225 · log(P) -4.711073336013072
Other tokens : 0.03619063936489409 · 49144 omitted candidates.
These tokens are neither in the top 8 nor the imposed target. The target’s probability counts once, even when it appears in both panels. other_probability = mass outside UNION(top8, target).
The maximum breaks ties by lowest ID. The script imposes the target, even when they coincide. Temperature 1, no filters or sampling.
The ordinary loop, unlike our example
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
Hi, I will be a little late. Sorry to
g=12 → 13 · ID 1446
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant Hi, I will be a little late. Sorry to
1, 9690, 198, 2683, 359, 253, 5356, 5646, 11173, 3365, 3511, 308, 34519, 28, 7018, 411, 407, 19712, 8182, 2, 198, 1, 4093, 198, 19161, 253, 3714, 288, 1303, 2206, 699, 339, 523, 325, 3008, 30, 2, 198, 1, 520, 9531, 198, 26843, 28, 339, 523, 325, 253, 1838, 3008, 30, 25442, 541, 288
Recorded model calculations — given the displayed prefix · Step prefix 12 · Documented reduced view
Final representation at the last position → logits → full-vocabulary softmax.
Probability ≠ truth. These scores concern continuation of the prefix, not the reliability of a claim.
Model maximum (argmax)
Ġhear · ID 4875 · rank / rang 1Logit 19.18226623535156 · P 0.3051657244781808 · log(P) -1.18690029099589
The most likely for this prefix. It does not decide which token is appended in our example.
Imposed choice — not selected by the model
Ġkeep · ID 1446 · rank / rang 21Logit 15.25147533416748 · P 0.00598983632353836 · log(P) -5.117691192179972
Target outside top 8 — not promoted in the ranking.
Imposed choice — not selected by the model ·Ġkeep · ID 1446From representation to probabilities
d0: -0.5657979249954224 d1: -1.826673150062561 d2: -1.1230062246322632 d3: 0.407820463180542 d4: 0.2504291236400604 d5: -0.6481521129608154 d6: -1.0134414434432983 d7: -2.165100574493408
Top 8 candidates and imposed target. Fixture values displayed by the browser, scientific notation when needed; no renormalization. Full-vocabulary logsumexp: 20.369166526347453
Ġhear· ID 4875 · rank / rang 1Logit 19.18226623535156 · P 0.3051657244781808 · log(P) -1.18690029099589
Ġbreak· ID 2373 · rank / rang 2Logit 18.23504638671875 · P 0.11834867488903218 · log(P) -2.1341201396287026
Ġbother· ID 18531 · rank / rang 3Logit 17.561723709106445 · P 0.060359144574573455 · log(P) -2.8074428172410073
Ġbump· ID 22908 · rank / rang 4Logit 17.550188064575195 · P 0.05966686356921321 · log(P) -2.8189784617722573
Ġthink· ID 1510 · rank / rang 5Logit 17.540145874023438 · P 0.05907067608004241 · log(P) -2.829020652324015
Ġget· ID 820 · rank / rang 6Logit 16.91200065612793 · P 0.031518964388736165 · log(P) -3.457165870219523
Ġdisturb· ID 7297 · rank / rang 7Logit 16.869901657104492 · P 0.030219590609247338 · log(P) -3.4992648692429604
Ġbe· ID 325 · rank / rang 8Logit 16.679821014404297 · P 0.02498835126897151 · log(P) -3.689345511943156
Other tokens : 0.3046721738184629 · 49143 omitted candidates.
These tokens are neither in the top 8 nor the imposed target. The target’s probability counts once, even when it appears in both panels. other_probability = mass outside UNION(top8, target).
The maximum breaks ties by lowest ID. The script imposes the target, even when they coincide. Temperature 1, no filters or sampling.
The ordinary loop, unlike our example
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
Hi, I will be a little late. Sorry to keep
g=13 → 14 · ID 346
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant Hi, I will be a little late. Sorry to keep
1, 9690, 198, 2683, 359, 253, 5356, 5646, 11173, 3365, 3511, 308, 34519, 28, 7018, 411, 407, 19712, 8182, 2, 198, 1, 4093, 198, 19161, 253, 3714, 288, 1303, 2206, 699, 339, 523, 325, 3008, 30, 2, 198, 1, 520, 9531, 198, 26843, 28, 339, 523, 325, 253, 1838, 3008, 30, 25442, 541, 288, 1446
Recorded model calculations — given the displayed prefix · Step prefix 13 · Documented reduced view
Final representation at the last position → logits → full-vocabulary softmax.
Probability ≠ truth. These scores concern continuation of the prefix, not the reliability of a claim.
Model maximum (argmax)
Ġyou · ID 346 · rank / rang 1Logit 19.786155700683594 · P 0.9542618840985152 · log(P) -0.04681713357161144
The most likely for this prefix. It does not decide which token is appended in our example.
Imposed choice — not selected by the model
Ġyou · ID 346 · rank / rang 1Logit 19.786155700683594 · P 0.9542618840985152 · log(P) -0.04681713357161144
Target already in the top 8; its probability is counted only once.
Imposed choice — not selected by the model ·Ġyou · ID 346From representation to probabilities
d0: -0.5804969668388367 d1: -2.092398166656494 d2: -0.4034739136695862 d3: -0.2988728880882263 d4: -1.1881353855133057 d5: -0.13915109634399414 d6: 0.38976895809173584 d7: -0.5138341784477234
Top 8 candidates and imposed target. Fixture values displayed by the browser, scientific notation when needed; no renormalization. Full-vocabulary logsumexp: 19.832972834255205
Ġyou· ID 346 · rank / rang 1Logit 19.786155700683594 · P 0.9542618840985152 · log(P) -0.04681713357161144
Ġeveryone· ID 2573 · rank / rang 2Logit 14.890088081359863 · P 0.00713398886490547 · log(P) -4.942884752895342
Ġon· ID 335 · rank / rang 3Logit 14.530855178833008 · P 0.004981034615424339 · log(P) -5.302117655422197
Ġpeople· ID 701 · rank / rang 4Logit 14.043898582458496 · P 0.0030608144086935237 · log(P) -5.789074251796709
Ġit· ID 357 · rank / rang 5Logit 13.720077514648438 · P 0.0022141309068167787 · log(P) -6.112895319606768
Ġme· ID 549 · rank / rang 6Logit 13.581164360046388 · P 0.001926966114660211 · log(P) -6.2518084742088185
Ġto· ID 288 · rank / rang 7Logit 13.44144344329834 · P 0.0016756914532827397 · log(P) -6.391529390956865
Ġu· ID 326 · rank / rang 8Logit 13.291946411132812 · P 0.0014430066032812478 · log(P) -6.541026423122393
Other tokens : 0.023302482934420325 · 49144 omitted candidates.
These tokens are neither in the top 8 nor the imposed target. The target’s probability counts once, even when it appears in both panels. other_probability = mass outside UNION(top8, target).
The maximum breaks ties by lowest ID. The script imposes the target, even when they coincide. Temperature 1, no filters or sampling.
The ordinary loop, unlike our example
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
Hi, I will be a little late. Sorry to keep you
g=14 → 15 · ID 7003
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant Hi, I will be a little late. Sorry to keep you
1, 9690, 198, 2683, 359, 253, 5356, 5646, 11173, 3365, 3511, 308, 34519, 28, 7018, 411, 407, 19712, 8182, 2, 198, 1, 4093, 198, 19161, 253, 3714, 288, 1303, 2206, 699, 339, 523, 325, 3008, 30, 2, 198, 1, 520, 9531, 198, 26843, 28, 339, 523, 325, 253, 1838, 3008, 30, 25442, 541, 288, 1446, 346
Recorded model calculations — given the displayed prefix · Step prefix 14 · Documented reduced view
Final representation at the last position → logits → full-vocabulary softmax.
Probability ≠ truth. These scores concern continuation of the prefix, not the reliability of a claim.
Model maximum (argmax)
Ġwaiting · ID 7003 · rank / rang 1Logit 26.402332305908203 · P 0.858207166551665 · log(P) -0.1529097557818062
The most likely for this prefix. It does not decide which token is appended in our example.
Imposed choice — not selected by the model
Ġwaiting · ID 7003 · rank / rang 1Logit 26.402332305908203 · P 0.858207166551665 · log(P) -0.1529097557818062
Target already in the top 8; its probability is counted only once.
Imposed choice — not selected by the model ·Ġwaiting · ID 7003From representation to probabilities
d0: 0.2854216992855072 d1: -2.1910338401794434 d2: -0.1800287663936615 d3: 1.2428923845291138 d4: 0.29130080342292786 d5: -1.5335160493850708 d6: 0.180755153298378 d7: -0.8591192960739136
Top 8 candidates and imposed target. Fixture values displayed by the browser, scientific notation when needed; no renormalization. Full-vocabulary logsumexp: 26.55524206169001
Ġwaiting· ID 7003 · rank / rang 1Logit 26.402332305908203 · P 0.858207166551665 · log(P) -0.1529097557818062
Ġup· ID 614 · rank / rang 2Logit 24.415771484375 · P 0.1177171486562268 · log(P) -2.1394705773150093
Ġon· ID 335 · rank / rang 3Logit 22.057783126831055 · P 0.011137261117958024 · log(P) -4.497458934858955
Ġso· ID 588 · rank / rang 4Logit 20.341081619262695 · P 0.0020008955124155513 · log(P) -6.214160442427314
Ġin· ID 281 · rank / rang 5Logit 20.16312789916992 · P 0.0016747118430241522 · log(P) -6.3921141625200875
Ġawaiting· ID 28121 · rank / rang 6Logit 19.40749740600586 · P 0.0007866362176931138 · log(P) -7.14774465568415
Ġoff· ID 767 · rank / rang 7Logit 19.16392517089844 · P 0.0006165834455731841 · log(P) -7.391316890791572
Ġawake· ID 22576 · rank / rang 8Logit 19.057186126708984 · P 0.0005541606479624322 · log(P) -7.498055934981025
Other tokens : 0.007305436007481138 · 49144 omitted candidates.
These tokens are neither in the top 8 nor the imposed target. The target’s probability counts once, even when it appears in both panels. other_probability = mass outside UNION(top8, target).
The maximum breaks ties by lowest ID. The script imposes the target, even when they coincide. Temperature 1, no filters or sampling.
The ordinary loop, unlike our example
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
Hi, I will be a little late. Sorry to keep you waiting
g=15 → 16 · ID 17
<|im_start|>system You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> <|im_start|>user Write a message to let someone know I will be late.<|im_end|> <|im_start|>assistant Hi, I will be a little late. Sorry to keep you waiting
1, 9690, 198, 2683, 359, 253, 5356, 5646, 11173, 3365, 3511, 308, 34519, 28, 7018, 411, 407, 19712, 8182, 2, 198, 1, 4093, 198, 19161, 253, 3714, 288, 1303, 2206, 699, 339, 523, 325, 3008, 30, 2, 198, 1, 520, 9531, 198, 26843, 28, 339, 523, 325, 253, 1838, 3008, 30, 25442, 541, 288, 1446, 346, 7003
Recorded model calculations — given the displayed prefix · Step prefix 15 · Documented reduced view
Final representation at the last position → logits → full-vocabulary softmax.
Probability ≠ truth. These scores concern continuation of the prefix, not the reliability of a claim.
Model maximum (argmax)
. · ID 30 · rank / rang 1Logit 22.8670654296875 · P 0.6672956779003861 · log(P) -0.4045220360898263
The most likely for this prefix. It does not decide which token is appended in our example.
Imposed choice — not selected by the model
! · ID 17 · rank / rang 3Logit 20.82699203491211 · P 0.0867612287175501 · log(P) -2.444595430865217
Target already in the top 8; its probability is counted only once.
Imposed choice — not selected by the model ·! · ID 17From representation to probabilities
d0: 0.33285361528396606 d1: -1.1384018659591677 d2: -0.47933995723724365 d3: 2.0314106941223145 d4: 1.1137346029281616 d5: -0.7825533747673035 d6: -2.0709948539733887 d7: -1.766234278678894
Top 8 candidates and imposed target. Fixture values displayed by the browser, scientific notation when needed; no renormalization. Full-vocabulary logsumexp: 23.271587465777326
.· ID 30 · rank / rang 1Logit 22.8670654296875 · P 0.6672956779003861 · log(P) -0.4045220360898263
,· ID 28 · rank / rang 2Logit 21.341018676757812 · P 0.14506566325691453 · log(P) -1.9305687890195136
!· ID 17 · rank / rang 3Logit 20.82699203491211 · P 0.0867612287175501 · log(P) -2.444595430865217
Ġfor· ID 327 · rank / rang 4Logit 20.000686645507812 · P 0.03797220553133754 · log(P) -3.270900820269514
Ġso· ID 588 · rank / rang 5Logit 19.153820037841797 · P 0.016280822005639187 · log(P) -4.117767427935529
Ġand· ID 284 · rank / rang 6Logit 18.485294342041016 · P 0.008343327813971972 · log(P) -4.786293123736311
Ġbut· ID 564 · rank / rang 7Logit 17.500877380371094 · P 0.0031175430062211373 · log(P) -5.7707100854062325
Ġa· ID 253 · rank / rang 8Logit 17.101383209228516 · P 0.0020908088954878805 · log(P) -6.170204256548811
Other tokens : 0.03307272287249243 · 49144 omitted candidates.
These tokens are neither in the top 8 nor the imposed target. The target’s probability counts once, even when it appears in both panels. other_probability = mass outside UNION(top8, target).
The maximum breaks ties by lowest ID. The script imposes the target, even when they coincide. Temperature 1, no filters or sampling.
The ordinary loop, unlike our example
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
Hi, I will be a little late. Sorry to keep you waiting!
No KV cache in this capture; the visit replays prefixes, it does not recalculate the model.
Recorded calculations for successive prefixes; tokens imposed by the script. End of prepared path — not a model-selected stop.
Go deeper
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
The scored prefix contains the initial input and only the tokens already imposed. No KV cache in this capture. The prepared ending is neither a chosen EOS nor a reached length limit.
S09 · The prepared reply returns to the screen
Is each bit appearing on screen one new model token?
Prepared reply — decoded from the imposed path. The model did not freely choose this text; the return is staged.
Generating tokens, transporting data and showing text are different steps. Their timing is simplified in this demonstration.
- You, at the same deskThe journey returns to its starting point.
- ApplicationDisplays the decoded text.
- Prepared replyWritten by our team, not freely chosen by the model.
Prepared reply — decoded from the imposed path. The model did not freely choose this text; the return is staged.
Show me
Must a network chunk, a token and a visible character be the same thing?
Prepared reply — decoded from the imposed path. The model did not freely choose this text; the return is staged.
End of prepared path — not a model-selected stop: prepared_sequence_end · 16 tokens. No EOS appended, no invented terminal distribution.
Output IDs and decoding
26843, 28, 339, 523, 325, 253, 1838, 3008, 30, 25442, 541, 288, 1446, 346, 7003, 17
Hi, I will be a little late. Sorry to keep you waiting!
Decoding: skip_special_tokens=false; clean_up_tokenization_spaces=false. No special token appended or measured network chunks. Character ≠ token ≠ packet.
Formatting and transport need not deliver one token at a time. A visible character may require more than one token.
Go deeper
The exact decoder options, handling of special tokens and any normalization belong to the capture manifest. A readable view must retain access to the raw IDs and decoded text, including spaces and line breaks.
The return-to-screen animation is not a network recording or a latency measurement. It may replay an already completed output; it must not claim the model is still computing.
S10 · Explain the mechanism and our exception
What would ordinary generation do differently from our authored example?
The application prepares an input. A trained model calculates possible next tokens; a decoding rule chooses one, adds it to the context, and repeats. The application displays the resulting text.
The imposed-path demonstration does not establish autonomous generation or factual truth.
- The application preparesMessage, envelope and tokens.
- The model calculatesRepresentations, then conditional scores.
- The application displaysHere, the tokens of the reply written by our team.
The imposed-path demonstration does not establish autonomous generation or factual truth.
Show me
What has to happen before the next token can be chosen?
Five distinctions to keep
- Server ≠ model
The computer hosts the software that runs the learned calculation.
Revisit S02 - Tokenization ≠ understanding
Splitting text and assigning IDs prepares calculation; it does not prove understanding.
Revisit S04 - Training ≠ generation
Learning adjusts parameters before the conversation. They stay fixed during this reply.
Revisit S03 - Probability ≠ truth
A high continuation score does not check facts. The maximum is not a guarantee.
Revisit S07 - Guided ≠ autonomous
Our team wrote the reply and imposed its tokens. Recorded conditional calculations do not prove the model would have chosen this text.
Revisit S08
The journey does not complete any lesson for you. Continue with the six existing lessons. Open the lessons
Explain it aloud or to yourself. We do not collect or save your explanation.
Before the model processes the request…
The input is prepared now; the learned parameters already exist. See S02–S03.
Before selecting the next token…
Scores concern continuations under this context, not factual verification. See S06–S07.
In ordinary generation, after a token is chosen…
The token extends the prefix for the next calculation. Only in our example does the script impose that token. See S08.
When the reply appears…
Display is not a truth test, and its timing is not the generation timing. See S09–S10.
Show a reference explanation
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand. Recorded calculations for successive prefixes; tokens imposed by the script. End of prepared path — not a model-selected stop. The imposed-path demonstration does not establish autonomous generation or factual truth.
The imposed-path demonstration does not establish autonomous generation or factual truth.
Go deeper
A model is not the entire assistant. An application may add search, tools or external memory, with their own behaviour and permissions. Only show such capabilities as used when the trace actually contains them.
The six existing lessons and their labs remain available now. They deepen generation, tokens, variation, hallucinations, context and systems; their prepared simulations are not this model's captured trace.
This is the complete reading version. Interactive controls require JavaScript; the explanations and sources do not.
Existing site · Explore the six lessons
Explore the six lessons
Provenance, sources and limits of this capture
Introduction of the original editorial proposal, now bound to verified data:
Our team wrote the example reply to keep the story easy to follow. This proposed journey would show model calculations along that imposed path, not a freely generated reply. No model is called during a visit.
In ordinary autoregressive generation, a decoding rule chooses the next token from scores for the current context, extends that context, and repeats. It does not require a complete reply written beforehand.
HuggingFaceTB/SmolLM2-135M-Instruct · 12fd25f77366fa6b3b4b768ec3050bf629380bac · Apache-2.0 · float32 · CPU · PyTorch 2.8.0+cpu · Transformers 4.57.6. Reproducibility seed 0, no sampling. Not a live capture.
SHA256 5af571cbf074e6d21a03528d2330792e532ca608f24ac70a143f6b369968ab8c
journey-guided-trace-v1 · late-authored-v1 · en
Trace manifest · Model / tokenizer
One head and one layer of the initial prompt, eight selected dimensions; no weights shipped. Diagrams and camera do not represent measured transport or latency.