File size: 3,337 Bytes
0a2f792 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | dataset_id: av_sft_Qwen3-8B_L24_6a22e324__av_sft_raw__explained__shuf42
stage: av_sft
row_count: 247261
extraction:
base_model: Qwen/Qwen3-8B
d_model: 4096
layer_index: 24
norm: none
corpus: /workspace-vast/celeste/nla-data/finefineweb_100k.parquet
corpus_slice:
start: 0
length: 100000
positions_per_doc: 10
kind: nla_dataset
schema_version: 1
keep_debug_metadata: true
tokens:
injection_char: "\u320E"
injection_token_id: 149705
injection_left_neighbor_id: 29
injection_right_neighbor_id: 522
critic_suffix_ids: null
prompt_templates:
actor: 'You are a meticulous AI researcher conducting an important investigation
into activation vectors from a language model. Your overall task is to describe
the semantic content of that activation vector.
We will pass the vector enclosed in <concept> tags into your context. You must
then produce an explanation for the vector, enclosed within <explanation> tags.
The explanation consists of 2-3 text snippets describing that vector.
Here is the vector:
<concept>{injection_char}</concept>
Please provide an explanation.'
critic: 'Summary of the following text: <text>{explanation}</text> <summary>'
api_summaries:
model: claude-sonnet-4-6
max_tokens: 300
temperature: 1.0
instruction_prompt: "A language model needs to predict what text comes next after\
\ a snippet which will be presented to you shortly. Identify the 2-3 most important\
\ features it would use for this prediction.\nFocus on what the language model\
\ must be \"thinking about\" at the point where the provided text ends. You should\
\ not need to reference the fact that the text is truncated/incomplete/a prefix:\
\ the language model is causal, so only sees the prefix to what it predicts and\
\ this is implicit.\nOrder features by what is most important for predicting the\
\ next tokens. Each feature should consist of a concise ~10-20 word description.\
\ Feel free to include specific textual examples inline.\n\nFeature types to consider\
\ (as inspiration, not a rigid checklist):\n- Syntactic/structural constraints:\
\ \"unclosed parenthesis requires matching close\"\n- Immediate semantic expectations:\
\ \"list promised three items but only two given\"\n- Stylistic/register patterns:\
\ \"formal academic tone maintained throughout\"\n- Narrative/argumentative momentum:\
\ \"thesis stated, supporting evidence now expected\"\n- Domain/genre signals:\
\ \"medical case history following SOAP format\"\n- Repetition/continuation patterns:\
\ \"same phrase structure repeating with variations\"\n\nThe final feature must\
\ describe the very end of the presented sequence: its role, what it's part of,\
\ and immediate constraints on what follows.\n\nFormat \u2014 IMPORTANT: keep\
\ to ~80-100 words total and ALWAYS close the tag:\n<analysis>\n[first feature\
\ \u2014 include specific examples when relevant]\n[second feature]\n[final feature:\
\ the last token, its role, immediate constraints]\n</analysis>\n\nText to analyze:\n\
\n<begin_text>{text}<end_text>"
parent_datasets:
- av_sft_Qwen3-8B_L24_6a22e324__av_sft_raw__explained
created_at: '2026-05-16T19:56:05.443944+00:00'
created_by: nla.datagen.stage_shuffle
git_commit: 047eb8e
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