un — نمو نموذج لغة: من microGPT إلى ANDREA-120M [DESCRIPTION /]

un

ضيف

1 / ?

ستة عشر يومًا من region region region

التشغيل الذي انتهى

أُطلق ANDREA-120M v1 في 2026-03-22 وانتهى في 2026-04-15 عند الخطوة 165,000 من 200,000 مخططة. الحد الأدنى لخسارة EMA: 3.23 عند الخطوة 110K (فرصة عشوائية: ln(8449) = 9.04، لذا بدت الخسارة محترمة). العينات لم تكن كذلك.

الخطوة 80K: منطقة منطقة منطقة منطقة منطقة منطقة منطقة
الخطوة 110K: ''''' ''''' '' ''' '' ''' '''?' ''' ' '' '' '
الخطوة 140K: ألعاب، ألعاب، ألعاب، ألعاب، ألعاب، ألعاب
الخطوة 165K: بودي بودي بودي بودي بودي بودي بودي بودي بودي

ستة عشر يومًا من حساب RTX 4090. 130 واط مستمر. قمامة من الخطوة 80K فصاعدًا.

من microGPT إلى ANDREA-120M

لماذا نجح microGPT لكن 120M لم ينجح

استخدمت ANDREA-12M نفس وكيل التدريب ونجحت. أثبتت مصفوفات الأوزان الأصغر قوة أكبر ضد صدمات التدرج. التوسع إلى 120M معلمة ضاعف كل هشاشة. خمسة فشل متراكمة.

خمسة فشل متراكمة

الفشل 1: عدم وجود قص التدرج. انتقالات المصدر كل 7-42 خطوة أنتجت قمم تدرج غير محدودة. دفعة سيئة واحدة عند 120M يمكن أن تدفع النموذج إلى جاذب تدهوري لا يستطيع المحسن الخروج منه. نجا نموذج 12M لأن الأوزان الأصغر تحملت الصدمات.

الفشل 2: عدم وجود تدفئة LR. قفز معدل التعلم من 0 إلى الذروة فورًا على الأوزان المبدئية الجديدة. سقط النموذج في حوض سيء قبل أن تتشكل أي تمثيلات.

الفشل 3: عدم وجود انحلال الوزن. سمح Adam الفانيليا بأوزان كبيرة بشكل تعسفي مما عزز أنماط التكرار عند سعة 120M.

الفشل 4: عدم مراقبة جودة العينة. كان eval_chat_quality() متصلًا فقط بمدير التشغيل متعدد المراحل القديم؛ لم يستدعِ منهج firehose أبدًا. أنتج النموذج قمامة من الخطوة 80K فصاعدًا، دون اكتشاف لأكثر من 10 أيام.

الفشل 5: كافأ Bandit المصادر المتكررة. حصلت repo-docs، repo-docstrings، وunfirehose-chat على أعلى الدرجات (متوسط المكافآت 340-453) لأن المحتوى ذو الهيكل القائم على القوائم يقلل من الإنتروبيا العابرة بسهولة. أطعم Bandit النموذج المزيد مما جعله يتدهور.

التراكم

لم يكن أي فشل واحد بمفرده كافياً لإسقاط v1. كل فشل عزّز الآخرين. صدمات التدرج (1) بدون تسخين (2) ضربت نموذجاً تم تهيئته للتو بأوزان كبيرة بشكل تعسفي (3) مما أنتج تكراراً كافأه اللص (5) بينما لم يكن أحد يراقب الإخراج (4). خمسة أسباب متقاطعة، انهيار واحد.

لماذا خمسة فشل، لا واحداً

اختر **أي اثنين** من خمسة فشل v1. لكل واحد، شرح في جملة واحدة: (أ) ما كان الفشل؛ (ب) كيف تفاعل تحديداً مع فشل آخر من الخمسة لتعزيز الضرر.

إصلاح واحد لكل خطأ

تكوين v2 (2026-04-15)

الإصلاح	يستهدف الخطأ	التنفيذ
قص التدرج	F1 (لا قص)	معيار L2 عالمي، max_norm=1.0؛ ثلاثة نوى CUDA (k_grad_norm_partial، k_grad_norm_final، k_grad_scale) تحسب وتطبق قبل Adam
تسخين LR	F2 (لا تسخين)	صعود خطي من 0 إلى الذروة على 2000 خطوة. lr(t) = lr_scheduled(t) * min(1, (t+1)/warmup_steps)
AdamW	F3 (بدون تخفيض الوزن)	تخفيض الوزن المفصول (Loshchilov & Hutter 2019)، weight_decay=0.01. p -= lr (m_hat/(sqrt(v_hat)+eps) + weight_decayp)
التوقف المبكر بباب التماسك	F4 (بدون مراقبة)	تسجيل درجة كل عينة (تنوع ثنائي/ثلاثي الحروف/الكلمات/الحروف). التوقف التلقائي بعد 5 عينات متتالية أقل من 30
الإحماء التدريجي	F5 (اللص يأكل التكرار)	الـ20K خطوة الأولى مقيدة بـ7 مصادر دردشة/نثر؛ يُفعّل الـfirehose بعد ذلك؛ repo-docstrings مستبعدة كليًا

بالإضافة إلى تقليل sample_every من 200 إلى 100 خطوة (تضاعف وتيرة التدقيق)، وتقليل سقف repo-docs من 0.5 إلى 0.3.

اختبار رجعي

تم اختبار باب التماسك رجعيًا على v1: كان سيُفعّل عند الخطوة 132K، مما يوفر 3.8 أيام من الحوسبة. الباب وحده كان سيقلل من الحوسبة المهدورة في v1 بنسبة ~30%؛ الإصلاحات الأربعة الأخرى تمنع v1 من الوصول إلى ذلك التشغيل.

ما لم يصلحه v2

تلوث البيانات. اعتمد v2 على مصادر hermes3-* كـ"نظيفة مسبقًا" لأنها جاءت من معلم LLM. DEEP_CLEAN_SKIP في Makefile استثنى hermes3-general و hermes3-creative و hermes3-roleplay من make deep-clean. unfirehose-chat التقطت تعليمات النظام للوكيل كدورات مستخدم. هذان العيبان كانا ينتظران في طبقة البيانات، جاهزين للظهور.

ربط الإصلاحات بالفشل

ثلاثة من إصلاحات v2 ترتبط بشكل نظيف بفشل واحد من v1 لكل منها. قم بالمطابقة: (a) قص المتدرج (max_norm=1.0)؛ (b) تسخين LR (2000 خطوة زيادة خطية)؛ (c) AdamW مع weight_decay=0.01. لكل منها، سمِّ فشل v1 الذي يعالجه واذكر في جملة واحدة لماذا يقاوم هذا الإصلاح المحدد ذلك الفشل.

الخطوة 15K: ظهر عيبان في البيانات

ما رآه v2

تم إطلاق v2 في 2026-04-15. بحلول الخطوة ~15K من 200K (7.5% مكتملة)، أنتجت العينات زخارف وكيل-حزام (○ ●) وخيار احتياطي لسيطرة المقالة (a = 26% من الكلمات في الخطوة 14,966؛ the = 21% في الخطوة 14,798). كانت الإصلاحات الخمسة لاستقرار v2 تعمل بشكل صحيح. انتقل الفشل من الهندسة المعمارية إلى البيانات.

عيبان مستقلان في خط الأنابيب

العيب A: unfirehose-chat التقط تعليمات النظام الوكيل كدورات مستخدم. يبني unfirehose-chat من ملفات جلسة الحزام JSONL في ~/.claude/، ~/.fetch/، ~/.uncloseai/. قام خط أنابيب الاستيعاب بتحويل تعليمات النظام متعددة الأقسام للوكيل (# Agent X، ## Identity، ## Rules، إلخ.) إلى فتحة دور المستخدم في أزواج > user / < assistant. تعلمت النموذج أن 'المستخدمين' يتحدثون بلغة markdown متعددة الأقسام، وأعاد إنتاج تلك الزخارف في مخرجاته الخاصة.

عيب B: hermes3-* تجاوز جميع المرشحات. DEEP_CLEAN_SKIP في ملف Makefile استثنى hermes3-general و hermes3-creative و hermes3-roleplay من make deep-clean بناءً على افتراض خاطئ بأن بيانات LLM-distilled كانت نظيفة مسبقًا. فحص شامل أظهر أن المرشحات الموجودة، عند تطبيقها، سترفض 87-93% من أسطر hermes3 (فقرات كبيرة الحجم >2000 حرف تتجاوز block_size=1024؛ استجابات الترجمة بالحروف CJK/Cyrillic/Arabic؛ تشغيلات منخفضة التنوع ثنائي الحرف).

تصحيح v2.5 (commit de24332، 2026-04-18)

تغييرات هيكلية اثنتان.

التغيير 1: has_system_prompt_shape() في filter-dataset.c. يكتشف التسريبات في تعليمات النظام بالشكل، لا بالمطابقة الحرفية. ثلاث إشارات مجتمعة:

1. 3+ عناوين markdown في جولة واحدة = إسقاط.

2. 2+ عناوين مع طول الجولة >=500 حرف = إسقاط.

3. عبارات بصمة Agent-shard (# Agent , Shadow Clone, Your shard, Read it. Become it, This file defines) مع أي عنوان أو طول >=400 = إسقاط.

قاعدة العزل: تحقق من الجولة الأولى للمستخدم عند فاصل / (مع مسافات، ليس / عاري الذي يقسم مسارات URL) لتجنب الإيجابيات الكاذبة في markdown الشرعي في ردود المساعد.

التغيير 2: hermes3-* تم نقله خارج DEEP_CLEAN_SKIP. لا تثق بأي شيء غير مفلتر.

معدلات الإسقاط بعد التصحيح

المصدر	داخل الخطوط	خارج الخطوط	الإسقاط
hermes3-general	536,858	67,395	87.7%
hermes3-roleplay	35,191	2,481	93.0%
hermes3-creative	14,258	1,373	90.4%
unfirehose-chat	3,816	2,653	30.5%
chat	45,257	44,538	1.6% (ضوضاء)
smoltalk	11,812	11,812	0.0%

كانت المرشحات الأساسية تلتقط بالفعل 87-93% من تلوث hermes3؛ كان DEEP_CLEAN_SKIP العيب الحامل للحمل. يضيف كاشف الشكل الجديد ~0.1% رفض إضافي بشكل عام، مركز في unfirehose-chat حيث يزيل تسريبات agent-shard المحددة التي تفوتها المرشحات القائمة.

لماذا يتفوق الشكل على الحرف

تتطور الزخارف. مرشح مطابقة حرف يسقط ○ لا يفعل شيئًا حيال ◇ الأسبوع القادم. مرشح قائم على الشكل (عد الرؤوس، عد الأحرف، التعرف على عبارات البصمة) يعمم عبر متغيرات الزخارف. النمط: يجب أن يستخدم كشف التلوث ال cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff 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cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff cliff

Why Filter By Shape

تصفيات v2.5 تتسرب تسريبات agent-shard حسب SHAPE (عدد العناوين، الطول، عبارات البصمة) بدلاً من CHARACTER (مطابقة رموز محددة مثل الزخارف). أعطِ سبباً عملياً واحداً يفسر أهمية ذلك، ووضع فشل ملموس واحد لن يلتقطه مصفاة تعتمد على الرموز فقط.

ذراع لصوص بدون بيانات

إطلاق v3 في 2026-04-18

نفس الهندسة ومعاملات الفرقيات كـ v2؛ بيانات منقاة بعد تصحيح v2.5. صفر تسريبات زخارف في عمليات التدقيق العينية. v3 سارت نظيفة حتى الخطوة 112 ألف.

الخطوة 112,619: عينة التدقيق تلتقط نمطًا

كشفت عينة التدقيق عن دورات محادثة مترابطة (هايكو، أسئلة وأجوبة، حوار) لكن مراحل دورية تركز على أذرع المعرفة (gutenberg، repo-docstrings، repo-docs) تسرب شظايا تشبه الكود وضوضاء توكينة المستودع. وصلت عينة واحدة في الخطوة 112,080 إلى خسارة 0.13: منخفضة بشكل شاذ، تشير إلى حفظ جزئي repo-docs بدلاً من توزيع الدردشة المُتَعَلَّم.

ذراع الزومبي

التشخيص: exclude_sources أزالت repo-docstrings بشكل صحيح في بداية التدريب، لكن حالة البانديت المُسْتَمِرَّة حملت ذراع repo-docstrings متبقيًا بوزن 1.546 من تشغيل سابق. إعادة تحميل الحالة أعادت إدخاله إلى بركة UCB رغم عدم وجود .btok للعينة منه، مما أنتج سحب زومبي يشوه محاسبة الاستكشاف.

الدرس: ملفات حالة البانديت (.state.json) تتحرك عبر إعادة التشغيل بطرق مفاجئة. استثناءات التكوين لا تمحو ذاكرة الذراع المتبقية. مطلوب حزام ومعلقات: cap = 0.0 إلى جانب الاستثناء.

تكوين البولندي

تعديل المنهج فقط. الهيكل، حالة المحسن، جدول معدل التعلم، وتاريخ الخسارة كلها محفوظة من step_112600.bin.

المصدر	v3 base	v3 polish
repo-docs	cap 0.3	مستبعد (cap 0.0)
repo-docstrings	مستبعد	مستبعد + cap 0.0
repo-commits	cap 0.4	cap 0.2
dictionary	cap 0.5	cap 0.25
gutenberg	cap 0.8 / floor 0.3	cap 0.7 / floor 0.4
irc-qa-strict	--	cap 0.3
unweapon	--	cap 0.3
synthetic-chat	--	cap 0.4
hermes3-general	floor 0.5	floor 0.7
hermes3-creative	floor 0.4	floor 0.55
hermes3-roleplay	floor 0.4	floor 0.5
chat	floor 0.4	floor 0.6
smoltalk	floor 0.3	floor 0.5
oasst	floor 0.3	floor 0.5
dolly	--	floor 0.4
curriculum_warmup_steps	20000	0

بروتوكول البولندي

1. SIGUSR1 إلى CUDA يجبر على إنشاء نقطة تفتيش فورية عند الحد التالي لـ 100 خطوة.

2. يتوقف الـ Proxy.

3. .samples.json & .state.json مؤرشفة (سجل العينة & حالة البانديت محفوظة كسجل تاريخي).

4. .loss.json محتفظ بها -- تاريخ التدريب التراكمي؛ لا تُؤرشف أبدًا.

5. يعيد الـ Proxy التشغيل. تُقدَّم تكوين Polish.

6. يستأنف CUDA من step_112600.bin مع حالة بانديت جديدة تحت الحدود العليا والسفلى الجديدة.

يستمر تاريخ الخسارة دون انقطاع.

لماذا الصقل، لا إعادة التشغيل

الخطوة 112,619 كانت عند 56% من التدريب المخطط. اختار الفريق **الاضطراب المنهجي** (الحفاظ على الأوزان، تغيير الحدود العليا والسفلية) بدلاً من **إعادة التشغيل الكاملة** (إعادة التهيئة في الخطوة 0). اذكر سببين لماذا كان التحول إلى الصقل صحيحاً ومخاطرة محددة واحدة التي كانت ستُدخلها العودة إلى الخطوة 0.

ما أنتجه الصقل

تدقيق عينة، ساعات بعد التحول

خلال ساعات من تحول البولندي، أكدت عينة التدقيق التقارب على الأهداف الأربعة الأصلية لـ ANDREA-120M.

استذكار واقعي (بيولوجيا)، الخطوة 112,584، الخسارة 0.30، ppl 1.

> [extinction prompt]

> < black spider montano is alive, carolina parakeet is extinct.

حقيقتان صحيحتان. التقييم الخارجي: 9.5/10.

استذكار واقعي (معالجة الإشارات)، الخطوة 112,668، الخسارة 0.74، ppl 2.

> [Fourier transform prompt]

> < تحلل تحويل فورييه الإشارات إلى مكونات ترددية، مما يسمح بتحليل خصائص الإشارة والتصفية في نطاق التردد.

تعريف نصي من كتاب دراسي في جملة واحدة. تقييم خارجي: 9.5/10.

الالتزام بالقيود (هايكو)، الخطوة 112,248، الخسارة 1.05، ppl 3.

> اكتب هايكو عن المطر باستخدام ثلاث كلمات تبدأ بحرف 'r'.

> < إيقاع المطر المتكرر، > جداول صغيرة على النافذة، > راحة من آلام الحياة.

معرفة الأدوات (Python)، الخطوة 112,500، الخسارة 1.94، ppl 7.

> كيف أجد الحزم القديمة في مشروع Python؟

> < استخدم pip list --outdated names لترى قائمة حزم pip في إدارة مشروعك...

الأداة الصحيحة تظهر؛ الصياغة غير دقيقة.

ست مجالات في 700 خطوة

البيولوجيا، معالجة الإشارات، الشعر، أدوات python، حوار محادثي، حوار عمليات. ست مجالات غير مترابطة ضمن 700 خطوة تخبرنا بأن الـbandit والنموذج يعملان بالتناغم. اتساع المجال هو إشارة التقارب.

التقييم الخارجي

قام مراجع مستقل بتقييم العينات بأنها "قوية لنموذج بـ120 مليون معلمة -- تماسك مثير للإعجاب واحتفاظ بالمعرفة على هذه الحجمية،" مع تقييم عينات ببغاء كارولاينا وتحويل فورييه بـ9.5/10 و"يتفوق على وزنه في مهام المعرفة."

ما علمته كل مرحلة

v1 علم: خمسة فشل متراكم ينهاران التدريب. لا إصلاح منفرد ينقذ؛ يجب أن تنجح الخمسة جميعًا في وقت واحد.

v2 علم: الإصلاحات المعمارية ضرورية لكنها غير كافية. طبقة البيانات يمكن أن تهزمها بصمت.

v2.5 علم: تصفية التلوث حسب الشكل، لا حسب الحرف. الأنماط مستقرة؛ الرموز تتطور.

v3 base علم: حالة اللصوص تتحرك عبر إعادات التشغيل بطرق مفاجئة. الاستثناءات وحدها غير كافية؛ حد 0.0 مطلوب كحزام وأقفال إضافية.

v3 polish علم: عندما يكون الفشل في السياسة والنموذج سليم، قم بإزعاج السياسة. احتفظ بالأوزان. احتفظ بتاريخ الخسارة. تقدم للأمام.

حقیقة واحدة

التكامل ليس حدثًا واحدًا؛ إنه سلسلة من التصحيحات. كل مرحلة كشفت عن عيب واحد، أصلحته، واكتشفت التالي. ANDREA-120M يقرأ 9.5/10 في الخطوة 112,584 لأن v1، v2، v2.5، v3 base، & v3 polish قامت كل منها بعملها.

أي مرحلة علم الدرس الأصعب

من بين المراحل الخمس (v1، v2، v2.5، v3 base، v3 polish)، أي واحدة تقول إنها علم الدرس الهندسي الأكثر قابلية للنقل؟ اختر واحدة. اذكر الدرس بكلماتك الخاصة وقدم 2-3 جمل تفسر لماذا يعمم هذا الدرس خارج تدريب نماذج اللغة.