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Model: deepakdsoni/antahkarana-7B Source: Original Platform
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Antahkarana-7B
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==============
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Author: Deepak Soni (Hugging Face: deepakdsoni)
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Architecture design, continual-learning training (saṃskāra / vijñāna-smṛti / pramāṇa),
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and merging of the model are the work of the author.
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Base model: Mistral-7B-v0.1 (© Mistral AI), used as a frozen foundation under Apache-2.0.
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See the NOTICE file for license attribution.
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NOTICE
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NOTICE
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Antahkarana-7B
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Copyright 2026 Deepak Soni (deepakdsoni)
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This model is a derivative work built on:
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Mistral-7B-v0.1
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https://huggingface.co/mistralai/Mistral-7B-v0.1
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Copyright Mistral AI, licensed under the Apache License, Version 2.0.
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Modifications made by the author:
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Continual instruction-tuning across four domains using the Antaḥkaraṇa
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architecture (saṃskāra consolidation, vijñāna-smṛti replay, pramāṇa
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abstention gate) via LoRA adapters, subsequently merged into the full
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model weights.
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The base Mistral-7B-v0.1 weights were used as a frozen foundation and were
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NOT trained from scratch in this work.
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This product is licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at:
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed
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under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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CONDITIONS OF ANY KIND, either express or implied.
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README.md
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---
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license: apache-2.0
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base_model: mistralai/Mistral-7B-v0.1
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- continual-learning
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- lifelong-learning
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- catastrophic-forgetting
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- self-supervised-learning
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- antahkarana
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- vedic-ai
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language:
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- en
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---
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# Antahkarana-7B
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> **A lifelong-learning language model that learns new domains without forgetting old ones, and abstains
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> instead of hallucinating when it is unsure** — built on Mistral-7B with an AI architecture derived from the
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> 2,500-year-old Vedic model of mind (the *antaḥkaraṇa*, "inner instrument").
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**Author:** Deepak Soni · **License:** Apache-2.0 · **Base model:** Mistral-7B-v0.1
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This is a **standalone, full-weights 7B model**: load it with a plain `from_pretrained` — no adapter, no PEFT.
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The continual-learning architecture was trained in via LoRA and then **merged into the base weights**.
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---
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## 📦 Model family
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| Model | What |
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|---|---|
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| [**antahkarana-v1**](https://huggingface.co/deepakdsoni/antahkarana-v1) | the original architecture + v1 vision models — the most stable continual learner (only positive backward transfer) |
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| [**antahkarana-v2**](https://huggingface.co/deepakdsoni/antahkarana-v2) | accuracy-recovering v2 (36.5M) — matches SOTA accuracy at ~3× less forgetting |
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| [**antahkarana-7B**](https://huggingface.co/deepakdsoni/antahkarana-7B) | the architecture scaled to a **7B language model** |
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## At a glance — what makes this different
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Standard fine-tuning suffers **catastrophic forgetting**: teach a model a new task and it loses the old one.
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Antahkarana-7B is trained with a small set of cognitive "faculties," each derived from a Vedic concept and
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implemented as a concrete mechanism:
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| Faculty (Vedic) | Mechanism (ML) | What it does |
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|---|---|---|
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| **saṃskāra** | Fisher-importance consolidation + decay over LoRA | protects what mattered for old domains → don't forget |
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| **vijñāna-smṛti** | dark-knowledge / exemplar replay | rehearses past domains while learning new ones |
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| **pramāṇa** | calibrated-confidence gate | abstains ("I'm not sure") instead of hallucinating |
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| **manas / buddhi** | two decorrelated views, cross-teaching | safe self-learning from unlabeled data (research track) |
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### How it works
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The borrowed mind (**Mistral-7B**) stays frozen as the stable core (*śruti*); a small trainable instrument
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(*chitta* = LoRA, ~0.2% of params) learns new domains, guided by the faculties — and the **pramāṇa** gate
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decides whether to answer or abstain:
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**Measured outcome (continual instruction-tuning, 4 domains, 3 seeds): ~3.8× less forgetting than naive LoRA,
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with higher and far more stable accuracy.**
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---
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## The journey: from a 2,500-year-old architecture to a 7B model
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This model is the production endpoint of a multi-stage research-to-engineering program.
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**1. The architecture.** The Vedic tradition describes the mind as an *antaḥkaraṇa* — an "inner instrument" of
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distinct faculties (chitta/memory, manas/perception, buddhi/discernment, ahaṃkāra/identity, plus pramāṇa/valid
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knowledge and the guṇa dynamics). Each faculty was mapped to a concrete, testable ML mechanism.
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**2. Research validation (vision, 36–52M params).** The mechanisms were first proven on continual-learning
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image benchmarks (Split-CIFAR-100, Split-Tiny-ImageNet) against the field's standard methods (EWC, ER, DER++):
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the architecture was the **most stable learner tested and the only one with positive backward transfer**, with
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a clean ablation showing each Vedic-derived component adds value.
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**3. Scaling on a frozen modern backbone (E1–E2).** On a **frozen ViT-B/16**, the consolidation works in
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*adapter space*, matching the SOTA (DER++) on accuracy while forgetting less, and extends to the harder
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**class-incremental** setting with label-free novelty detection (avidyā).
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**4. Self-learning and memory (E-S, śruti/smṛti/nidrā).** The model learns from **unlabeled** data via
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decorrelated co-training and reaches near-supervised accuracy from ~2% labels; a complementary study showed an
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external "smṛti" memory + periodic "sleep" consolidation retains knowledge ~2.4× better than holding it in
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weights.
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**5. The 7B model (E7).** The architecture was ported to language: **frozen Mistral-7B + LoRA + saṃskāra +
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vijñāna-smṛti + pramāṇa**, continually instruction-tuned across four domains with checkpointing, then **merged**
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into the standalone 7B model published here.
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---
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## Results
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### Continual instruction-tuning — naive LoRA vs Antaḥkaraṇa-LoRA (3-seed mean ± std)
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Four text-classification domains learned in sequence (AG News → DBpedia → Emotion → SST-2), each with its own
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label space, so forgetting is meaningful.
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| Metric | naive LoRA | **Antaḥkaraṇa (this model)** |
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|---|---|---|
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| Final accuracy ↑ | 0.849 ± .029 | **0.882 ± .003** |
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| **Forgetting** ↓ | 0.053 ± .032 | **0.014 ± .009** (~3.8× less) |
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| Confidence on known domains | 0.841 | **0.954** |
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| Known − unknown confidence gap ↑ | 0.467 | **0.494** |
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### Live deployment test (this merged model)
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- **General language preserved** — correct world-knowledge answers (e.g. capital of Japan → Tokyo; a fluent
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one-sentence definition of photosynthesis).
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- **Continual retention: 8/8 correct across all four domains, including the first one learned** — no
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catastrophic forgetting, demonstrated live.
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- **pramāṇa abstention** — on a factually neutral input (no sentiment to extract), confidence drops to 0.53 and
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the model abstains rather than guessing; on clear inputs it stays 0.97–0.99 and answers.
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---
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## Why this is an innovation in today's AI
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Most of modern AI is **static**: a model is trained once, frozen, and shipped. Teaching it something new means
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expensive retraining — and naive fine-tuning *overwrites* old knowledge (catastrophic forgetting). The field's
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strongest continual-learning methods buy stability only by trading away accuracy, or vice-versa.
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Antaḥkaraṇa breaks that trade-off. Across a rigorous benchmark vs the standard methods (EWC, LwF, ER, DER++), it
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is the **only method that lands in the "ideal corner" — high accuracy *and* very low forgetting** — matching the
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SOTA's accuracy while forgetting ~3× less:
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That combination is what makes a model genuinely *lifelong*: it can keep learning in deployment without
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expensive retraining and without losing what it already knew — while the **pramāṇa** gate lets it say
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*"I don't know"* instead of hallucinating. A static, occasionally-confident model becomes a **living, honest**
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one. That is the shift the architecture is reaching for.
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## Potential — and where it needs to adapt
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**What this architecture could unlock:**
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- **Lifelong enterprise models** — absorb new products, policies, and data continuously, without retraining the
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base or forgetting prior knowledge.
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- **Trustworthy / high-stakes AI** — calibrated abstention (pramāṇa) for medical, legal, and financial settings
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where "I'm not sure" is safer than a confident guess.
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- **Label-efficient & self-learning** — learns from *unlabeled* data (co-training), reaching near-supervised
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accuracy from as little as ~2% labels — cutting annotation cost dramatically.
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- **Personal / on-device AI** — a tiny adapter (~160 MB) + external memory personalizes a frozen base to a user,
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privacy-preserving, with no full retraining.
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- **Agentic memory** — the śruti (stable core) / smṛti (external memory) / nidrā (sleep-consolidation) design
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gives agents that accumulate experience over time.
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**Where it still needs to adapt (honest roadmap):**
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- **Beyond classification** — the LLM evaluation here is classification framed as generation; it needs extension
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to open-ended instruction-following and longer, more realistic domain streams.
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- **Sharper pramāṇa** — the abstention gate works but is over-confident on adversarial nonsense; it needs
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stronger calibration (e.g. conformal / ensemble methods) at scale.
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- **Scale & breadth** — validated on 4 domains and 7B; next is longer continual streams, established
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continual-LLM benchmarks, and larger models (13B–70B).
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- **Self-learning + memory at LLM scale** — co-training and the smṛti/nidrā memory are proven in vision and small
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setups; integrating them into the LLM continual loop is the next build.
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- **Conditional compute** — a guṇa-driven mixture-of-experts / early-exit layer (efficiency) is designed but not
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yet implemented.
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## Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tok = AutoTokenizer.from_pretrained("deepakdsoni/antahkarana-7B")
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model = AutoModelForCausalLM.from_pretrained(
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"deepakdsoni/antahkarana-7B", dtype=torch.bfloat16, device_map="auto")
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prompt = ("Classify the sentiment of this movie review (negative, positive).\n"
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"Text: a heartfelt, beautifully acted triumph.\nAnswer:")
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out = model.generate(**tok(prompt, return_tensors="pt").to(model.device),
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max_new_tokens=4, pad_token_id=tok.eos_token_id)
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print(tok.decode(out[0], skip_special_tokens=True))
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```
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Requires a GPU for full-precision inference (~15 GB in bf16); 4-bit quantization (bitsandbytes) runs in ~5 GB.
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---
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## Training details
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| | |
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|---|---|
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| **Base model** | `mistralai/Mistral-7B-v0.1` (frozen) |
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| **Adapter** | LoRA (r=16, α=32) on `q/k/v/o_proj`; ~13.6M trainable (0.19%) |
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| **Method** | saṃskāra (Fisher Ω + decay) on LoRA · vijñāna-smṛti exemplar replay · pramāṇa confidence gate |
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| **Curriculum** | 4 classification domains in sequence, per-task checkpointing (resumable) |
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| **Merge** | LoRA folded into base via `merge_and_unload` → standalone full-weights 7B |
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| **Precision** | bfloat16 |
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To **continue lifelong-learning** (add new domains with saṃskāra protection), use the LoRA adapter + resume
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workflow rather than this merged checkpoint — merging flattens the LoRA structure.
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---
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## Limitations & honest notes
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- Continual evaluation is on **classification framed as generation** (clean, measurable), not open-ended
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instruction following — a natural next extension.
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- The pramāṇa gate is **not perfect**: it abstains well on genuinely under-determined input but can still be
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over-confident on adversarial nonsense; the robust evidence is the calibration AUROC and the
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in-distribution-vs-unfamiliar confidence gap across many examples.
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- The model inherits the capabilities, biases, and knowledge cutoff of **Mistral-7B-v0.1**.
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---
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## License & attribution
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Released under the **Apache License 2.0**. This is a **derivative work of Mistral-7B-v0.1** (© Mistral AI,
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Apache-2.0) — see the `NOTICE` file. The base 7B weights were used as a **frozen foundation and were not trained
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from scratch**. The Antaḥkaraṇa architecture, continual training, and merging are the contribution of the author.
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*Built on the Upaniṣads, Sāṃkhya, Yoga, Nyāya, and modern ML (PyTorch · Transformers · PEFT).*
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## Citation
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```bibtex
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@misc{antahkarana7b2026,
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title = {Antahkarana-7B: Lifelong Learning with a Vedic-Derived Cognitive Architecture},
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author = {Deepak Soni},
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year = {2026},
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note = {Built on Mistral-7B-v0.1 (Apache-2.0)},
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url = {https://huggingface.co/deepakdsoni/antahkarana-7B}
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}
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```
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config.json
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config.json
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{
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"dtype": "bfloat16",
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"eos_token_id": 2,
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"head_dim": null,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"tie_word_embeddings": false,
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"transformers_version": "4.57.6",
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"use_cache": true,
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"vocab_size": 32000,
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"_name_or_path": "antahkarana-7b",
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"author": "Deepak Soni",
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"model_creator": "Deepak Soni"
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}
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.57.6"
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}
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||||
}
|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
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|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
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|
||||
},
|
||||
"unk_token": {
|
||||
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|
||||
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|
||||
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|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
268053
tokenizer.json
Normal file
268053
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
44
tokenizer_config.json
Normal file
44
tokenizer_config.json
Normal file
@@ -0,0 +1,44 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
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|
||||
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|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [],
|
||||
"bos_token": "<s>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"extra_special_tokens": {},
|
||||
"legacy": false,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": null,
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user