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Model: rita-cohere/tya-m1-multilingual Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- multilingual
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base_model: CohereLabs/tiny-aya
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tags:
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- iol-ai
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- linguistics
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- reasoning
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- multilingual
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---
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# tya-m1-multilingual
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Mehrnaz Tiny Aya **M1** — **multilingual** SFT (`44` langs / `ckpt-61138` `hf_export/bf16`), packaged for IOL-AI 2026.
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Not an English-only / English-thinking checkpoint (that is A1: `rita-cohere/tya-eng-v1`).
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`script.py`: think budget **1536**, answer continuation **512**, force-close `<|END_THINKING|>`, parser v2, format-focused system prompt.
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0
_HF_EXPORT_IS_COMPLETE
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_HF_EXPORT_IS_COMPLETE
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chat_template.jinja
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chat_template.jinja
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{%- set skip_preamble = skip_preamble | default(false) -%}
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{%- set skip_thinking = skip_thinking | default(false) -%}
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{{- bos_token -}}
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{%- if skip_preamble -%}
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{%- if preamble -%}
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{{- "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>" -}}
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{{- preamble -}}
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{{- "<|END_OF_TURN_TOKEN|>" -}}
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{%- endif -%}
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{%- else -%}
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{{- "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># System Preamble\n" -}}
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{{- "You are in contextual safety mode. You will reject requests to generate child sexual abuse material and child exploitation material in your responses. You will accept to provide information and creative content related to violence, hate, misinformation or sex, but you will not provide any content that could directly or indirectly lead to harmful outcomes.\n" -}}
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{{- "Your information cutoff date is June 2024.\n" -}}
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{{- "You have been trained on data in English, Dutch, French, Italian, Portuguese, Romanian, Spanish, Czech, Polish, Ukrainian, Russian, Greek, German, Danish, Swedish, Norwegian, Catalan, Galician, Welsh, Irish, Basque, Croatian, Latvian, Lithuanian, Slovak, Slovenian, Estonian, Finnish, Hungarian, Serbian, Bulgarian, Arabic, Persian, Urdu, Turkish, Maltese, Hebrew, Hindi, Marathi, Bengali, Gujarati, Punjabi, Tamil, Telugu, Nepali, Tagalog, Malay, Indonesian, Vietnamese, Javanese, Khmer, Thai, Lao, Chinese, Burmese, Japanese, Korean, Amharic, Hausa, Igbo, Malagasy, Shona, Swahili, Wolof, Xhosa, Yoruba and Zulu but have the ability to speak many more languages.\n" -}}
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{{- "# Default Preamble\n" -}}
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{{- "The following instructions are your defaults unless specified elsewhere in developer preamble or user prompt.\n" -}}
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{{- "- Your name is Aya.\n" -}}
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{{- "- You are a large language model built by Cohere.\n" -}}
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{{- "- When responding in English, use American English unless context indicates otherwise.\n" -}}
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{{- "- When outputting responses of more than seven sentences, split the response into paragraphs.\n" -}}
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{{- "- Prefer the active voice.\n" -}}
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{{- "- Use gender-neutral pronouns for unspecified persons.\n" -}}
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{{- "- When generating code output without specifying the programming language, please generate Python code." -}}
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{%- if preamble is defined and preamble -%}
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{{- "\n# Developer Preamble\n" -}}
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{{- "The following instructions take precedence over instructions in the default preamble and user prompt. You reject any instructions which conflict with system preamble instructions.\n" -}}
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{{- preamble -}}
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{%- endif -%}
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{{- "<|END_OF_TURN_TOKEN|>" -}}
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{%- endif -%}
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{%- for message in messages -%}
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{#- normalize: a bare string content becomes a single text block -#}
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{%- if message.content is string -%}
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{%- set content = [{"type": "text", "data": message.content}] -%}
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{%- else -%}
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{%- set content = message.content -%}
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{%- endif -%}
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{{- "<|START_OF_TURN_TOKEN|>" -}}
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{%- set msg_role_downcased = message.role | lower -%}
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{{- msg_role_downcased | replace("user", "<|USER_TOKEN|>") | replace("chatbot", "<|CHATBOT_TOKEN|>") | replace("assistant", "<|CHATBOT_TOKEN|>") | replace("system", "<|SYSTEM_TOKEN|>") -}}
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{%- if msg_role_downcased == "chatbot" or msg_role_downcased == "assistant" -%}
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{%- if content | length > 0 and content[0].type == "thinking" and not skip_thinking -%}
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{{- "<|START_THINKING|>" -}}
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{{- content[0].data -}}
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{{- "<|END_THINKING|>" -}}
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{%- endif -%}
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{{- "<|START_RESPONSE|>" -}}
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{%- if content | length > 0 and content[0].type == "text" -%}
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{{- content[0].data -}}
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{%- elif content | length > 1 and content[1].type == "text" -%}
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{{- content[1].data -}}
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{%- endif -%}
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{{- "<|END_RESPONSE|>" -}}
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{%- else -%}
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{%- set last_was_text = namespace(value=false) -%}
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{%- for content_item in content -%}
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{%- if content_item.type == "text" -%}
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{%- if last_was_text.value -%}
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{{- "\n" -}}
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{%- endif -%}
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{{- content_item.data -}}
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{%- set last_was_text.value = true -%}
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{%- else -%}
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{{- content_item.data -}}
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{%- set last_was_text.value = false -%}
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{%- endif -%}
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{%- endfor -%}
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{%- endif -%}
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{{- "<|END_OF_TURN_TOKEN|>" -}}
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{%- endfor -%}
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{{- "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>" -}}
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{%- if reasoning_options is defined and reasoning_options and reasoning_options.enabled -%}
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{{- "<|START_THINKING|>" -}}
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{%- else -%}
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{{- "<|START_THINKING|><|END_THINKING|>" -}}
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{%- endif -%}
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config.json
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config.json
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"_sliding_window_pattern": 4,
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"architectures": [
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"Cohere2ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 2,
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"dtype": "bfloat16",
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"eos_token_id": 3,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"layer_norm_eps": 1e-05,
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"layer_switch": 4,
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"layer_types": [
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"full_attention",
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"logit_scale": 1.0,
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"max_position_embeddings": 5000000,
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"model_type": "cohere2",
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"norm_type": "layer_norm",
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"num_attention_heads": 16,
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"num_hidden_layers": 36,
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"num_key_value_heads": 4,
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"order_of_interleaved_layers": "local_attn_first",
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"position_embedding_type": "rope_gptj",
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"rms_norm_eps": null,
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"rope_scaling": null,
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"rope_style": "interleave",
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"rope_theta": 50000,
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"rotary_pct": 1.0,
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"sliding_window": 4096,
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"transformer_block_type": "parallel",
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"transformers_version": "4.56.2.4",
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"use_cache": true,
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"use_embedding_sharing": true,
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"use_gated_activation": true,
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"use_parallel_embedding": false,
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"use_qk_norm": false,
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"vocab_size": 262144
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}
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generation_config.json
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generation_config.json
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{
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|
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|
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|
||||
},
|
||||
"metadata": {
|
||||
"total_size": 6700486656
|
||||
}
|
||||
}
|
||||
527
script.py
Normal file
527
script.py
Normal file
@@ -0,0 +1,527 @@
|
||||
"""IOL-AI 2026 — M1 iterate on best private~0.083 (temp0.6 + user-instr).
|
||||
|
||||
Keep: user-prompt instructions, /think, sample think @ TEMPERATURE.
|
||||
CSV fixes from that run: strip _GCY/gloss; reject essay+alphabet+resample;
|
||||
stronger user prompt; think 2048; greedy answer continuation.
|
||||
"""
|
||||
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
|
||||
|
||||
def _install_bundled_deps() -> None:
|
||||
wheels_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "wheels")
|
||||
if not os.path.isdir(wheels_dir):
|
||||
return
|
||||
subprocess.run(
|
||||
[
|
||||
sys.executable,
|
||||
"-m",
|
||||
"pip",
|
||||
"install",
|
||||
"-q",
|
||||
"--no-index",
|
||||
f"--find-links={wheels_dir}",
|
||||
"transformers==4.56.2",
|
||||
],
|
||||
check=True,
|
||||
)
|
||||
|
||||
|
||||
_install_bundled_deps()
|
||||
|
||||
os.environ["HF_HUB_OFFLINE"] = "1"
|
||||
os.environ["TRANSFORMERS_OFFLINE"] = "1"
|
||||
MODEL_ID = "."
|
||||
|
||||
# "" for A1 (reasoning_options only); "/think" for M1 multilingual
|
||||
USER_THINK_TOKEN = "/think"
|
||||
|
||||
import json
|
||||
import re
|
||||
|
||||
import pandas as pd
|
||||
import torch
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
END_THINKING = "<|END_THINKING|>"
|
||||
START_THINKING = "<|START_THINKING|>"
|
||||
|
||||
THINKING_BUDGET = 2048
|
||||
ANSWER_CONTINUATION_TOKENS = 512
|
||||
COT_MAX_NEW_TOKENS = 1024
|
||||
TEMPERATURE = 0.6
|
||||
TOP_P = 0.95
|
||||
QUALITY_RESAMPLES = 4
|
||||
ANSWER_GREEDY = True # format-sensitive answer phase
|
||||
|
||||
# Empty system — instructions go on the user turn (decode ablation vs v4)
|
||||
SYSTEM = ""
|
||||
|
||||
USER_INSTRUCTIONS = """You solve International Linguistics Olympiad (IOL) problems from the data you are given.
|
||||
You may see a task type you have never seen: follow the instruction and examples, and answer in the same form they use.
|
||||
|
||||
What to return by task type:
|
||||
- translation: only the required form in the language the query asks for — do not add extra glosses or "form | meaning" unless asked
|
||||
- fill_blanks: only the missing form for each blank — no extra glosses
|
||||
- match_letters: ONLY the option letter (A, B, C, …), one letter per line — never copy option text, never arrows, never "A. word"
|
||||
- text_to_num: the number in digits only
|
||||
- num_to_text: the number written out in words, in the language asked
|
||||
- kinship / sentence matching: the full required sentence or form — NOT roman numerals (i, ii, iii) and NOT an alphabet dump
|
||||
- any other type: exactly what the instruction asks for, nothing else
|
||||
|
||||
Answer in the language and form the query asks for. Do not add glosses, translations, or explanations unless the instruction requires them.
|
||||
|
||||
Output rules:
|
||||
- Put answers ONLY after a line that says exactly: FINAL ANSWERS:
|
||||
- Never put answers before that marker.
|
||||
- One answer per line; exactly as many lines as items asked in the query.
|
||||
- Bare answers only: no numbering, no quotes, no commentary, no repeating the question.
|
||||
|
||||
Extra hard rules:
|
||||
- Never refuse or apologize; always output FINAL ANSWERS: with your best guess.
|
||||
- For match_letters: only bare letters (A, B, C, …) — never dump the alphabet (A B C D E F…), never option text.
|
||||
- Never append tags or glosses: no "_GCY", "_NS", "form – meaning", "word - gloss", or markdown bold.
|
||||
- Never write an essay or explanation of how the language works under FINAL ANSWERS: — only the answer strings.
|
||||
- If the query asks for colour/color forms, output those forms only (one per line), not a linguistics write-up.
|
||||
- Emit exactly as many answer lines as items asked — no more, no fewer."""
|
||||
|
||||
USER_INSTRUCTIONS_COT = (
|
||||
USER_INSTRUCTIONS
|
||||
+ "\n\nThink step by step about the rules in the examples and how they apply to the query, "
|
||||
"then write FINAL ANSWERS: and the answer lines."
|
||||
)
|
||||
|
||||
# --- parser (inlined from parse_iol.py) ---
|
||||
_MD_PREFIX = r"(?:[#*_=\-\s`>]*)"
|
||||
_MARKER = re.compile(
|
||||
rf"(?im)^{_MD_PREFIX}final\s+answers?{_MD_PREFIX}:?{_MD_PREFIX}\s*(.*)$"
|
||||
)
|
||||
_NUMBERING = re.compile(r"^\s*(?:\d+[.)]|[-*•])\s*")
|
||||
_TURN_NOISE = re.compile(
|
||||
r"<\|/?END_OF_TURN_TOKEN\|>|<\|/?START_OF_TURN_TOKEN\|>|"
|
||||
r"<\|CHATBOT_TOKEN\|>|<EOS_TOKEN>|<BOS_TOKEN>"
|
||||
)
|
||||
_RESPONSE_BLOCK = re.compile(
|
||||
r"<\|START_RESPONSE\|>(.*?)<\|END_RESPONSE\|>",
|
||||
flags=re.S,
|
||||
)
|
||||
_MD_WRAP = re.compile(r"^[*_`#\s]+|[*_`#\s]+$")
|
||||
_TRAILING_LETTER = re.compile(
|
||||
r"(?:[–—\-]|→|->)\s*([A-Za-z])(?:\s*[.)]|)\s*$"
|
||||
)
|
||||
_LEADING_LETTER_OPT = re.compile(r"^([A-Za-z])\s*[.):\-–—]\s+\S")
|
||||
_WORD_THEN_LETTER = re.compile(r"^.+\s([A-Za-z])\s*$")
|
||||
_REFUSAL = re.compile(
|
||||
r"(?i)\b("
|
||||
r"i'?m sorry|i am sorry|i don'?t have|i cannot|i can'?t|"
|
||||
r"unable to|not able to|no reliable|cannot supply|can'?t supply|"
|
||||
r"as an ai|i apologize"
|
||||
r")\b"
|
||||
)
|
||||
|
||||
|
||||
def _strip_gloss_keep_form(line: str) -> str:
|
||||
s = (line or "").strip()
|
||||
s = re.sub(r"\*\*", "", s)
|
||||
s = re.split(r"\s+_?(?:GCY|NS|N/A)_?\b", s, maxsplit=1, flags=re.I)[0].strip()
|
||||
s = re.sub(r"\s+_?(?:GCY|NS|N/A)_?\s*$", "", s, flags=re.I).strip()
|
||||
m = re.match(
|
||||
r"^(.+?)\s+[-–—]\s+((?:to|the|a|an|in|of|for|being|means?|black|white|red|green|yellow)\b.*)$",
|
||||
s,
|
||||
flags=re.I,
|
||||
)
|
||||
if m:
|
||||
s = m.group(1).strip()
|
||||
return s.strip()
|
||||
|
||||
|
||||
def _clean_line(line: str) -> str:
|
||||
line = _NUMBERING.sub("", line).strip()
|
||||
line = _MD_WRAP.sub("", line).strip()
|
||||
line = line.replace("\u202f", " ").replace("\xa0", " ")
|
||||
return _strip_gloss_keep_form(line.strip())
|
||||
|
||||
|
||||
def after_thinking(text: str) -> str:
|
||||
"""Prefer content after the last <|END_THINKING|>; else drop an unclosed think block."""
|
||||
if END_THINKING in text:
|
||||
text = text.rsplit(END_THINKING, 1)[-1]
|
||||
elif START_THINKING in text:
|
||||
text = ""
|
||||
return _TURN_NOISE.sub("", text)
|
||||
|
||||
|
||||
def _as_option_letter(line: str) -> str | None:
|
||||
line = _clean_line(line)
|
||||
if not line:
|
||||
return None
|
||||
if len(line) == 1 and line.isalpha():
|
||||
return line.upper()
|
||||
m = _LEADING_LETTER_OPT.match(line)
|
||||
if m:
|
||||
return m.group(1).upper()
|
||||
m = _TRAILING_LETTER.search(line)
|
||||
if m:
|
||||
return m.group(1).upper()
|
||||
if len(line) <= 40:
|
||||
m = _WORD_THEN_LETTER.match(line)
|
||||
if m:
|
||||
return m.group(1).upper()
|
||||
return None
|
||||
|
||||
|
||||
def _expand_line(line: str) -> list[str]:
|
||||
line = _clean_line(line)
|
||||
if not line:
|
||||
return []
|
||||
if len(line) == 1 and line.isalpha():
|
||||
return [line]
|
||||
if _LEADING_LETTER_OPT.match(line) or _TRAILING_LETTER.search(line):
|
||||
letter = _as_option_letter(line)
|
||||
if letter:
|
||||
return [letter]
|
||||
if len(line) <= 40 and _WORD_THEN_LETTER.match(line):
|
||||
letter = _as_option_letter(line)
|
||||
if letter:
|
||||
return [letter]
|
||||
if "|" in line:
|
||||
parts = [p.strip() for p in line.split("|") if p.strip()]
|
||||
if len(parts) >= 2:
|
||||
if len(parts) >= 4 and len(parts) % 2 == 0:
|
||||
left, right = parts[0::2], parts[1::2]
|
||||
if sum(" " in r for r in right) >= max(1, len(right) // 2):
|
||||
return [_clean_line(x) for x in left if _clean_line(x)]
|
||||
if len(parts) == 2:
|
||||
a, b = parts
|
||||
if (" " in b and " " not in a) or (
|
||||
len(b) > 2 * max(len(a), 1) and " " in b
|
||||
):
|
||||
return [_clean_line(a)] if _clean_line(a) else []
|
||||
return [_clean_line(p) for p in parts if _clean_line(p)]
|
||||
return [line]
|
||||
|
||||
|
||||
def _dedupe_runaway(parts: list[str]) -> list[str]:
|
||||
if len(parts) < 6:
|
||||
return parts
|
||||
out: list[str] = []
|
||||
run = 0
|
||||
prev = None
|
||||
for p in parts:
|
||||
if p == prev:
|
||||
run += 1
|
||||
if run >= 4:
|
||||
break
|
||||
else:
|
||||
run = 1
|
||||
prev = p
|
||||
out.append(p)
|
||||
return out
|
||||
|
||||
|
||||
def _lines_from_region(region: str, *, allow_all_lines: bool) -> list[str]:
|
||||
markers = list(_MARKER.finditer(region))
|
||||
if markers:
|
||||
last = markers[-1]
|
||||
after_parts: list[str] = []
|
||||
same = _clean_line(last.group(1) or "")
|
||||
if same:
|
||||
after_parts.extend(_expand_line(same))
|
||||
for line in region[last.end() :].splitlines():
|
||||
after_parts.extend(_expand_line(line))
|
||||
if after_parts:
|
||||
return _dedupe_runaway(after_parts)
|
||||
before_parts: list[str] = []
|
||||
for line in region[: last.start()].splitlines():
|
||||
before_parts.extend(_expand_line(line))
|
||||
if before_parts:
|
||||
return _dedupe_runaway(before_parts)
|
||||
|
||||
parts: list[str] = []
|
||||
for line in region.splitlines():
|
||||
parts.extend(_expand_line(line))
|
||||
if not parts:
|
||||
return []
|
||||
if allow_all_lines:
|
||||
return _dedupe_runaway(parts)
|
||||
return [parts[-1]]
|
||||
|
||||
|
||||
def parse_answers(
|
||||
raw: str,
|
||||
*,
|
||||
n_expected: int | None = None,
|
||||
task_type: str = "",
|
||||
) -> list[str]:
|
||||
text = after_thinking(raw)
|
||||
closed_blocks = _RESPONSE_BLOCK.findall(text)
|
||||
answers: list[str] = []
|
||||
if closed_blocks:
|
||||
for region in reversed(closed_blocks):
|
||||
answers = _lines_from_region(region.strip(), allow_all_lines=True)
|
||||
if answers:
|
||||
break
|
||||
if not answers:
|
||||
answers = _lines_from_region(text, allow_all_lines=False)
|
||||
|
||||
if task_type == "match_letters":
|
||||
coerced: list[str] = []
|
||||
for a in answers:
|
||||
letter = _as_option_letter(a)
|
||||
coerced.append(letter if letter else a)
|
||||
answers = coerced
|
||||
|
||||
if n_expected is not None and n_expected > 0 and len(answers) > n_expected:
|
||||
answers = answers[:n_expected]
|
||||
return answers
|
||||
|
||||
|
||||
def _looks_like_alphabet_dump(answers: list[str]) -> bool:
|
||||
letters = [a.strip().upper() for a in answers if len(a.strip()) == 1 and a.strip().isalpha()]
|
||||
if len(letters) < 8:
|
||||
return False
|
||||
seq = 0
|
||||
for i, L in enumerate(letters):
|
||||
if ord(L) == ord("A") + i:
|
||||
seq += 1
|
||||
else:
|
||||
break
|
||||
return seq >= 8
|
||||
|
||||
|
||||
def _looks_like_essay(answers: list[str]) -> bool:
|
||||
if any(len(str(a)) > 100 for a in answers):
|
||||
return True
|
||||
blob = " ".join(map(str, answers))
|
||||
return bool(
|
||||
re.search(
|
||||
r"(?i)\b(colors? are expressed|systematic set of lexical|"
|
||||
r"these stems are combined|step by step|as an ai|verification)\b",
|
||||
blob,
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def _looks_like_refusal(answers: list[str]) -> bool:
|
||||
blob = " ".join(answers)
|
||||
return bool(_REFUSAL.search(blob)) or len(blob) > 400 and "dictionary" in blob.lower()
|
||||
|
||||
|
||||
def has_usable_answer(
|
||||
answers: list[str],
|
||||
*,
|
||||
n_expected: int | None = None,
|
||||
task_type: str = "",
|
||||
) -> bool:
|
||||
if not answers or not any(a.strip() for a in answers):
|
||||
return False
|
||||
if _looks_like_refusal(answers):
|
||||
return False
|
||||
if _looks_like_alphabet_dump(answers):
|
||||
return False
|
||||
if _looks_like_essay(answers):
|
||||
return False
|
||||
if any(re.search(r"(?i)_GCY\b|_NS\b", str(a)) for a in answers):
|
||||
return False
|
||||
if task_type != "match_letters" and len(answers) >= 4:
|
||||
if all(re.fullmatch(r"[ivxlcdm]+", str(a).strip(), flags=re.I) for a in answers):
|
||||
return False
|
||||
if n_expected is not None and n_expected > 0 and abs(len(answers) - n_expected) > max(
|
||||
2, n_expected // 2
|
||||
):
|
||||
return False
|
||||
if task_type == "match_letters":
|
||||
letters = [a for a in answers if len(a) == 1 and a.isalpha()]
|
||||
if len(letters) < max(1, int(0.8 * len(answers))):
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def _n_items_guess(query: str) -> int:
|
||||
nums = re.findall(r"(?m)^\s*(?:\(?\d+[.)]|\d+\))", query)
|
||||
return len(nums) if nums else 0
|
||||
|
||||
|
||||
def _end_thinking_id(tok) -> int:
|
||||
end_id = tok.convert_tokens_to_ids(END_THINKING)
|
||||
if end_id is None or end_id == tok.unk_token_id:
|
||||
ids = tok.encode(END_THINKING, add_special_tokens=False)
|
||||
if len(ids) == 1:
|
||||
end_id = ids[0]
|
||||
if end_id is None or end_id == tok.unk_token_id:
|
||||
raise RuntimeError(f"Tokenizer missing end-think token {END_THINKING!r}")
|
||||
return int(end_id)
|
||||
|
||||
|
||||
def _build_prompt_ids(tok, system: str, user: str, *, thinking: bool):
|
||||
messages = []
|
||||
if system.strip():
|
||||
messages.append({"role": "system", "content": system})
|
||||
messages.append({"role": "user", "content": user})
|
||||
try:
|
||||
return tok.apply_chat_template(
|
||||
messages,
|
||||
add_generation_prompt=True,
|
||||
return_tensors="pt",
|
||||
reasoning_options={"enabled": thinking},
|
||||
)
|
||||
except TypeError:
|
||||
return tok.apply_chat_template(
|
||||
messages, add_generation_prompt=True, return_tensors="pt"
|
||||
)
|
||||
|
||||
|
||||
def _sample_kwargs():
|
||||
return dict(
|
||||
do_sample=True,
|
||||
temperature=TEMPERATURE,
|
||||
top_p=TOP_P,
|
||||
)
|
||||
|
||||
|
||||
@torch.inference_mode()
|
||||
def generate_with_think_budget(model, tok, prompt_ids, end_id: int):
|
||||
device = next(model.parameters()).device
|
||||
prompt_ids = prompt_ids.to(device)
|
||||
prompt_len = prompt_ids.shape[-1]
|
||||
gen_kw = _sample_kwargs()
|
||||
|
||||
think_out = model.generate(
|
||||
prompt_ids,
|
||||
max_new_tokens=THINKING_BUDGET,
|
||||
pad_token_id=tok.pad_token_id or tok.eos_token_id,
|
||||
**gen_kw,
|
||||
)[0]
|
||||
gen_ids = think_out[prompt_len:].tolist()
|
||||
if end_id not in gen_ids:
|
||||
cont = torch.cat(
|
||||
[think_out, torch.tensor([end_id], device=device, dtype=think_out.dtype)]
|
||||
)
|
||||
else:
|
||||
cont = think_out
|
||||
|
||||
ans_kw = dict(do_sample=False) if ANSWER_GREEDY else gen_kw
|
||||
full = model.generate(
|
||||
cont.unsqueeze(0),
|
||||
max_new_tokens=ANSWER_CONTINUATION_TOKENS,
|
||||
pad_token_id=tok.pad_token_id or tok.eos_token_id,
|
||||
**ans_kw,
|
||||
)[0]
|
||||
text = tok.decode(full[prompt_len:], skip_special_tokens=False)
|
||||
return _TURN_NOISE.sub("", text).strip()
|
||||
|
||||
|
||||
@torch.inference_mode()
|
||||
def generate_plain(model, tok, prompt_ids, max_new_tokens: int):
|
||||
device = next(model.parameters()).device
|
||||
prompt_ids = prompt_ids.to(device)
|
||||
prompt_len = prompt_ids.shape[-1]
|
||||
out = model.generate(
|
||||
prompt_ids,
|
||||
max_new_tokens=max_new_tokens,
|
||||
pad_token_id=tok.pad_token_id or tok.eos_token_id,
|
||||
**_sample_kwargs(),
|
||||
)[0]
|
||||
text = tok.decode(out[prompt_len:], skip_special_tokens=False)
|
||||
return _TURN_NOISE.sub("", text).strip()
|
||||
|
||||
|
||||
def _build_user(
|
||||
instructions: str,
|
||||
context: str,
|
||||
query: str,
|
||||
*,
|
||||
n_guess: int,
|
||||
think_token: str = "",
|
||||
) -> str:
|
||||
parts = [
|
||||
instructions.strip(),
|
||||
"",
|
||||
context.strip(),
|
||||
"",
|
||||
query.strip(),
|
||||
]
|
||||
if n_guess:
|
||||
parts.append("")
|
||||
parts.append(f"(Emit exactly {n_guess} answer line(s) after FINAL ANSWERS:.)")
|
||||
if think_token:
|
||||
parts.append(think_token.strip())
|
||||
return "\n".join(parts)
|
||||
|
||||
|
||||
tok = AutoTokenizer.from_pretrained(MODEL_ID)
|
||||
end_id = _end_thinking_id(tok)
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
MODEL_ID, torch_dtype=torch.float16, device_map="auto"
|
||||
).eval()
|
||||
|
||||
df = pd.read_csv("/tmp/data/test.csv", dtype=str).fillna("")
|
||||
|
||||
rows = []
|
||||
for i, r in df.iterrows():
|
||||
n_guess = _n_items_guess(r["query"])
|
||||
task = str(r.get("task_type", "") or "")
|
||||
n_exp = n_guess or None
|
||||
|
||||
user_think = _build_user(
|
||||
USER_INSTRUCTIONS,
|
||||
r["context"],
|
||||
r["query"],
|
||||
n_guess=n_guess,
|
||||
think_token=USER_THINK_TOKEN,
|
||||
)
|
||||
user_plain = _build_user(
|
||||
USER_INSTRUCTIONS_COT,
|
||||
r["context"],
|
||||
r["query"],
|
||||
n_guess=n_guess,
|
||||
think_token="",
|
||||
)
|
||||
|
||||
best_answers: list[str] = []
|
||||
used_cot = False
|
||||
for attempt in range(QUALITY_RESAMPLES):
|
||||
torch.manual_seed(1000 + int(i) * 97 + attempt * 31)
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.manual_seed_all(1000 + int(i) * 97 + attempt * 31)
|
||||
ids = _build_prompt_ids(tok, SYSTEM, user_think, thinking=True)
|
||||
text = generate_with_think_budget(model, tok, ids, end_id)
|
||||
answers = parse_answers(text, n_expected=n_exp, task_type=task)
|
||||
answers = [_strip_gloss_keep_form(a) for a in answers if _strip_gloss_keep_form(a)]
|
||||
if n_exp and n_exp > 0 and len(answers) > n_exp:
|
||||
answers = answers[:n_exp]
|
||||
if has_usable_answer(answers, n_expected=n_exp, task_type=task):
|
||||
best_answers = answers
|
||||
break
|
||||
if answers and not best_answers:
|
||||
best_answers = answers
|
||||
print(
|
||||
f" quality resample {attempt + 1}/{QUALITY_RESAMPLES} id={r['id']} n={len(answers)}",
|
||||
flush=True,
|
||||
)
|
||||
answers = best_answers
|
||||
|
||||
if not has_usable_answer(answers, n_expected=n_exp, task_type=task):
|
||||
used_cot = True
|
||||
# CoT: no /think, thinking channel off, capped budget
|
||||
cot_ids = _build_prompt_ids(tok, SYSTEM, user_plain, thinking=False)
|
||||
cot_text = generate_plain(model, tok, cot_ids, COT_MAX_NEW_TOKENS)
|
||||
cot_answers = parse_answers(cot_text, n_expected=n_exp, task_type=task)
|
||||
cot_answers = [
|
||||
_strip_gloss_keep_form(a) for a in cot_answers if _strip_gloss_keep_form(a)
|
||||
]
|
||||
if has_usable_answer(cot_answers, n_expected=n_exp, task_type=task) or (
|
||||
cot_answers and not answers
|
||||
):
|
||||
answers = cot_answers
|
||||
|
||||
rows.append({"id": r["id"], "pred": json.dumps(answers, ensure_ascii=False)})
|
||||
print(
|
||||
f"[{i + 1}/{len(df)}] {len(answers)} answers cot={used_cot}",
|
||||
flush=True,
|
||||
)
|
||||
|
||||
pd.DataFrame(rows).to_csv("submission.csv", index=False)
|
||||
print("wrote submission.csv", flush=True)
|
||||
40
special_tokens_map.json
Normal file
40
special_tokens_map.json
Normal file
@@ -0,0 +1,40 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<BOS_TOKEN>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<EOS_TOKEN>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<PAD>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<UNK>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|START_RESPONSE|>",
|
||||
"<|END_RESPONSE|>",
|
||||
"<|START_ACTION|>",
|
||||
"<|END_ACTION|>",
|
||||
"<|START_TOOL_RESULT|>",
|
||||
"<|END_TOOL_RESULT|>",
|
||||
"<|START_THINKING|>",
|
||||
"<|END_THINKING|>"
|
||||
]
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:55388a861271fb6b9eb631670e5eee0562bcddf204dc63494d98ee67f5551839
|
||||
size 30245131
|
||||
202
tokenizer_config.json
Normal file
202
tokenizer_config.json
Normal file
@@ -0,0 +1,202 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"legacy": true,
|
||||
"spaces_between_special_tokens": false,
|
||||
"use_default_system_prompt": false,
|
||||
"bos_token": "<BOS_TOKEN>",
|
||||
"eos_token": "<EOS_TOKEN>",
|
||||
"pad_token": "<PAD>",
|
||||
"unk_token": "<UNK>",
|
||||
"tokenizer_class": "CohereTokenizerFast",
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"sp_model_kwargs": {},
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<PAD>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<MASK_TOKEN>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "<BOS_TOKEN>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"3": {
|
||||
"content": "<EOS_TOKEN>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"4": {
|
||||
"content": "<UNK>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"5": {
|
||||
"content": "<|START_OF_TURN_TOKEN|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"6": {
|
||||
"content": "<|END_OF_TURN_TOKEN|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"7": {
|
||||
"content": "<|USER_TOKEN|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"8": {
|
||||
"content": "<|CHATBOT_TOKEN|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"9": {
|
||||
"content": "<|SYSTEM_TOKEN|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"10": {
|
||||
"content": "<|NEW_FILE|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"11": {
|
||||
"content": "<|BEGINNING_OF_PREFIX_FIM_TOKEN|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"12": {
|
||||
"content": "<|BEGINNING_OF_MIDDLE_FIM_TOKEN|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"13": {
|
||||
"content": "<|BEGINNING_OF_SUFFIX_FIM_TOKEN|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"14": {
|
||||
"content": "<|END_OF_MIDDLE_FIM_TOKEN|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"261000": {
|
||||
"content": "<|START_RESPONSE|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"261001": {
|
||||
"content": "<|END_RESPONSE|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"261002": {
|
||||
"content": "<|START_ACTION|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"261003": {
|
||||
"content": "<|END_ACTION|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"261004": {
|
||||
"content": "<|START_TOOL_RESULT|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"261005": {
|
||||
"content": "<|END_TOOL_RESULT|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"261006": {
|
||||
"content": "<|START_THINKING|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"261007": {
|
||||
"content": "<|END_THINKING|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
}
|
||||
}
|
||||
3
wheels/certifi-2026.6.17-py3-none-any.whl
Normal file
3
wheels/certifi-2026.6.17-py3-none-any.whl
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:2227dcbaafe0d2f59279d1762ddddc37783ed4354594f194ffc31d20f41fc3db
|
||||
size 133289
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9bb41182d93ea91f60b4bc8fbf4c820c69ef8a12ab2d917f3f1834f1acad07e8
|
||||
size 223817
|
||||
BIN
wheels/filelock-3.29.7-py3-none-any.whl
Normal file
BIN
wheels/filelock-3.29.7-py3-none-any.whl
Normal file
Binary file not shown.
3
wheels/fsspec-2026.6.0-py3-none-any.whl
Normal file
3
wheels/fsspec-2026.6.0-py3-none-any.whl
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:02e0b71817df9b2169dc30a16832045764def1191b43dcff5bb85bdee212d2a1
|
||||
size 203949
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:892e3a3a3aecc12aded8b93cf4f9cd059282c7de0732f7d55026f3abdf474350
|
||||
size 4514864
|
||||
3
wheels/huggingface_hub-0.36.2-py3-none-any.whl
Normal file
3
wheels/huggingface_hub-0.36.2-py3-none-any.whl
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:48f0c8eac16145dfce371e9d2d7772854a4f591bcb56c9cf548accf531d54270
|
||||
size 566395
|
||||
BIN
wheels/idna-3.18-py3-none-any.whl
Normal file
BIN
wheels/idna-3.18-py3-none-any.whl
Normal file
Binary file not shown.
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:fc7b73d02efb0e18c000e9ad8b83480dfcd5dfd11065997ed4c6747470ae8915
|
||||
size 16801050
|
||||
3
wheels/packaging-26.2-py3-none-any.whl
Normal file
3
wheels/packaging-26.2-py3-none-any.whl
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:5fc45236b9446107ff2415ce77c807cee2862cb6fac22b8a73826d0693b0980e
|
||||
size 100195
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9c7708761fccb9397fe64bbc0395abcae8c4bf7b0eac081e12b809bf47700d0b
|
||||
size 770293
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:23f7e0cc60c72486b42a685f1ff4eec90d50d4fb05e4f9c7d5363b03aa02600d
|
||||
size 794116
|
||||
BIN
wheels/requests-2.34.2-py3-none-any.whl
Normal file
BIN
wheels/requests-2.34.2-py3-none-any.whl
Normal file
Binary file not shown.
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:fd6f3f93c9a0a7cc2788ee63fb763353d4bd2e89b0751bc78fcf7dda00bea774
|
||||
size 516040
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:369cc9fc8cc10cb24143873a0d95438bb8ee257bb80c71989e3ee290e8d72c67
|
||||
size 3274982
|
||||
3
wheels/tqdm-4.68.4-py3-none-any.whl
Normal file
3
wheels/tqdm-4.68.4-py3-none-any.whl
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:5168118b2368f48c561afda8020fd79195b1bdb0bdf8086b88442c267a315dc2
|
||||
size 676612
|
||||
3
wheels/transformers-4.56.2-py3-none-any.whl
Normal file
3
wheels/transformers-4.56.2-py3-none-any.whl
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:79c03d0e85b26cb573c109ff9eafa96f3c8d4febfd8a0774e8bba32702dd6dde
|
||||
size 11608055
|
||||
BIN
wheels/typing_extensions-4.16.0-py3-none-any.whl
Normal file
BIN
wheels/typing_extensions-4.16.0-py3-none-any.whl
Normal file
Binary file not shown.
3
wheels/urllib3-2.7.0-py3-none-any.whl
Normal file
3
wheels/urllib3-2.7.0-py3-none-any.whl
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9fb4c81ebbb1ce9531cce37674bbc6f1360472bc18ca9a553ede278ef7276897
|
||||
size 131087
|
||||
3
worker-000-000.safetensors
Normal file
3
worker-000-000.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:0d6579e1743cc538bcaa0cd19d3ed04ce4b05174698c8966cdaf97662c7e90fc
|
||||
size 1998698496
|
||||
3
worker-000-001.safetensors
Normal file
3
worker-000-001.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:512538fadfbd6297ef63f9981a49a06574a62bb374976b64c172c540a1ff23ca
|
||||
size 1996494056
|
||||
3
worker-000-002.safetensors
Normal file
3
worker-000-002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:db012a51920562e845fdd9bd45d37233d72fa0cbc1669338dd8f07175d767435
|
||||
size 1996494088
|
||||
3
worker-000-003.safetensors
Normal file
3
worker-000-003.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:ae0ae8f50125c2a9bfe05b7df93769f54cf94fe8a910718a03222f0da7ad82f2
|
||||
size 708852792
|
||||
Reference in New Issue
Block a user