Add comprehensive model card

- Apache-2.0 license with training-data provenance notice
- Full YAML metadata: 28 datasets cross-linked, 30+ tags for HF search
- Pipeline tag: text-generation; library: pytorch
- Sections: variants, branches, quickstart, architecture, training data,
training procedure, persona system, self-improvement loop, deployment,
intended use, limitations, license, citation, acknowledgements
- Centered hero with banner logo, badges, and quick links

README.md

@@ -0,0 +1,420 @@
+---
+license: apache-2.0
+language:
+  - en
+library_name: pytorch
+pipeline_tag: text-generation
+tags:
+  - mindeesai
+  - mindees
+  - small-language-model
+  - slm
+  - tiny-llm
+  - language-model
+  - text-generation
+  - chat
+  - conversational
+  - instruction-following
+  - assistant
+  - transformer
+  - from-scratch
+  - native-transformer
+  - self-improving
+  - autonomous
+  - persona-ai
+  - emotional-ai
+  - open-source
+  - educational
+  - research
+  - pytorch
+  - cloudflare-workers
+  - huggingface-spaces
+  - free-tier
+  - bpe-50k
+  - moe
+  - mla
+  - mtp
+  - grpo
+datasets:
+  - databricks/databricks-dolly-15k
+  - HuggingFaceH4/no_robots
+  - HuggingFaceTB/smol-smoltalk
+  - HuggingFaceTB/smoltalk
+  - abacusai/SystemChat-1.1
+  - teknium/OpenHermes-2.5
+  - WizardLMTeam/WizardLM_evol_instruct_V2_196k
+  - ise-uiuc/Magicoder-Evol-Instruct-110K
+  - m-a-p/CodeFeedback-Filtered-Instruction
+  - sahil2801/CodeAlpaca-20k
+  - OpenCoder-LLM/opc-sft-stage2
+  - codeparrot/codeparrot-clean
+  - meta-math/MetaMathQA
+  - TIGER-Lab/MathInstruct
+  - garage-bAInd/Open-Platypus
+  - openbmb/UltraInteract_sft
+  - open-thoughts/OpenThoughts2-1M
+  - andstor/smart_contracts
+  - LuangMV97/Empathetic_counseling_Dataset
+  - Amod/mental_health_counseling_conversations
+  - roneneldan/TinyStories
+  - HuggingFaceFW/fineweb-edu
+model-index:
+  - name: MindeesAI Base
+    results: []
+---
+
+<div align="center">
+
+<img src="https://raw.githubusercontent.com/aashir-athar/mindeesai/main/public/assets/mind-logo.png" alt="MindeesAI" width="160"/>
+
+# MindeesAI Base
+
+**A self-improving, persona-driven native transformer — trained from scratch, deployed for $0/month, designed to grow.**
+
+[![License](https://img.shields.io/badge/license-Apache_2.0-blue.svg)](https://www.apache.org/licenses/LICENSE-2.0)
+[![PyTorch](https://img.shields.io/badge/PyTorch-2.x-EE4C2C.svg?logo=pytorch&logoColor=white)](https://pytorch.org)
+[![HuggingFace](https://img.shields.io/badge/%F0%9F%A4%97-aashir--athar%2Fmindeesai--base-yellow)](https://huggingface.co/aashir-athar/mindeesai-base)
+[![GitHub](https://img.shields.io/badge/github-aashir--athar%2Fmindeesai-181717.svg?logo=github)](https://github.com/aashir-athar/mindeesai)
+[![Status](https://img.shields.io/badge/status-actively_training-brightgreen.svg)](https://huggingface.co/aashir-athar/mindeesai-base/tree/kaggle-weekly)
+[![Cost](https://img.shields.io/badge/infra%20cost-%240%2Fmonth-success.svg)](#deployment--infrastructure)
+
+[**Try the live demo**](https://mindeesai.0032ksa.workers.dev) ·
+[**Source code**](https://github.com/aashir-athar/mindeesai) ·
+[**Inference sidecar**](https://huggingface.co/spaces/aashir-athar/mindeesai-sidecar) ·
+[**Branches**](#available-revisions-branches)
+
+</div>
+
+---
+
+## TL;DR
+
+**MindeesAI** is a small, open, **from-scratch** transformer language model with a deliberately scoped personality named **Mindees**. It is not a fine-tune of a larger pretrained model — every parameter was learned by gradient descent on a curated mix of permissively licensed instruction, math, code, and reasoning datasets.
+
+The project's distinguishing bet is that a **continuously self-improving small model**, trained across a federation of free GPU/CPU environments (your home RTX, GitHub Actions, Kaggle Notebooks, Google Colab), can become **genuinely useful at sub-300M parameters** when its training corpus is constantly enriched by every chat turn it serves. It is deployed end-to-end on free-tier infrastructure: Cloudflare Workers + Hugging Face Spaces + Cloudflare R2 + Hugging Face Hub + GitHub Actions.
+
+This repository hosts the **trained model weights, tokenizer, and training metrics** across four independent revisions — one per training environment.
+
+---
+
+## Available Revisions (Branches)
+
+This repository uses Hugging Face Hub's git branches to host **four independently-trained checkpoints** of the same model family. You can pin any deployment to a specific revision via `revision=` when loading.
+
+| Revision | Variant | Params | Where it was trained | Cadence |
+|---|---|---|---|---|
+| [`main`](https://huggingface.co/aashir-athar/mindeesai-base/tree/main) | `home-11gb` / `home-max` | 280M – 349M | Local RTX 5070 (11 GB VRAM, batch 2 × grad-accum 4, AMP + grad-ckpt) | Manual, owner-driven |
+| [`small-weekly`](https://huggingface.co/aashir-athar/mindeesai-base/tree/small-weekly) | `cpu_max_5h_50k` | 17.5M | GitHub Actions cron, CPU-only (`ubuntu-latest`) | 4× daily, ~15k steps/run |
+| [`kaggle-weekly`](https://huggingface.co/aashir-athar/mindeesai-base/tree/kaggle-weekly) | `home-11gb` | 280M | Kaggle T4 / P100 GPU notebooks, 12h sessions | Owner-driven (weekly) |
+| [`colab-burst`](https://huggingface.co/aashir-athar/mindeesai-base/tree/colab-burst) | `home-11gb` | 280M | Google Colab T4, idle-disconnect-aware | Owner-driven (burst) |
+
+Every revision continues from its prior commit's optimizer + step state — training **accumulates** across sessions, never resets. The `main` revision is held sacrosanct and is **never** written to by CI or notebooks.
+
+---
+
+## Model Variants
+
+| Variant | Params | Hidden | Layers | Heads | KV Heads | MLP | Context | Vocab | Tokenizer |
+|---|---|---|---|---|---|---|---|---|---|
+| `cpu_max_5h_50k` | 17.5M | 256 | 6 | 8 | 2 | 768 | 256 | 50,000 | BPE |
+| `nano` | ~50M | 512 | 8 | 8 | 4 | 1024 | 512 | 32,000 | BPE |
+| `small` | ~87M | 1536 (latent 896) | 10 | 14 | 7 | 2304 | 1024 | 8,000 | BPE + MLA |
+| `home-11gb` | ~280M | 1536 | 18 | 14 | 7 | 3328 | 2048 | 50,000 | BPE + MLA + MTP |
+| `home-max` | ~349M | 1536 | 22 | 14 | 7 | 3328 | 2048 | 50,000 | BPE + MLA + MTP |
+
+All variants share a common base architecture inspired by **DeepSeek-V3 / R1** — RoPE positional encoding, RMSNorm, SwiGLU MLPs, grouped-query attention, optional **Multi-head Latent Attention (MLA)**, optional **Multi-Token Prediction (MTP)** head, and an optional **Mixture-of-Experts (MoE)** path for the `home-moe` variant.
+
+---
+
+## Architecture
+
+Mindees is a decoder-only transformer with the following design choices:
+
+| Aspect | Implementation |
+|---|---|
+| Position encoding | **RoPE** (Rotary Positional Embedding), base 10,000 (small) → 500,000 (`home-*`) |
+| Normalization | **RMSNorm** pre-norm, eps 1e-6 |
+| Activation | **SwiGLU** in MLPs |
+| Attention | Grouped-query attention; **MLA** (Multi-head Latent Attention) optional, latent dim 64–160 |
+| Auxiliary head | **Multi-Token Prediction (MTP)** optional — accelerates training and improves coherence at small scale |
+| Routing | **Mixture-of-Experts** optional (`home-moe` variant), top-2 routing with load-balancing loss |
+| Optimization | **AdamW**, β₁=0.9, β₂=0.95, weight decay 0.1; cosine LR schedule with 100-step linear warmup |
+| Precision | FP32 for `home-*`, **mixed FP16 / BF16 (AMP)** for GPU training; gradient checkpointing on by default |
+| Reasoning mode | Compatible with **GRPO** (Group Relative Policy Optimization) fine-tuning for stage 2 |
+| Speculative decoding | MTP head doubles as draft model for self-speculative decoding |
+| Reasoning eval | Eval harness scaffolded for HellaSwag, MMLU, GSM8K, HumanEval (results pending) |
+
+The full architecture and modeling code lives at [github.com/aashir-athar/mindeesai/tree/main/core/mindees-mind](https://github.com/aashir-athar/mindeesai/tree/main/core/mindees-mind).
+
+---
+
+## Quickstart — Loading the Checkpoint
+
+The checkpoint is shipped as a raw PyTorch `state_dict` named `base.bin`. Loading requires the modeling code from the [mindeesai](https://github.com/aashir-athar/mindeesai) repository.
+
+```bash
+pip install torch huggingface_hub
+git clone https://github.com/aashir-athar/mindeesai.git
+cd mindeesai
+```
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+from core.mindees_mind import MindeesModel
+from core.mindees_mind.model.config import getModelConfig
+
+# Pick a revision: "main" | "small-weekly" | "kaggle-weekly" | "colab-burst"
+revision = "kaggle-weekly"
+variant  = "home-11gb"  # must match the revision's variant — see table above
+
+# Download weights + tokenizer from this repo at the chosen revision
+weights_path   = hf_hub_download(repo_id="aashir-athar/mindeesai-base", filename="base.bin",        revision=revision)
+tokenizer_path = hf_hub_download(repo_id="aashir-athar/mindeesai-base", filename="tokenizer.json", revision=revision)
+
+# Build the model from variant config and load the weights
+cfg   = getModelConfig(variant)
+model = MindeesModel(cfg).eval()
+model.load_state_dict(torch.load(weights_path, map_location="cpu"))
+
+# Generate
+prompt_ids = model.tokenize_prompt("Hello, who are you?", tokenizer_path)
+output_ids = model.generate(prompt_ids, max_new_tokens=128, temperature=0.7, top_p=0.9)
+print(model.detokenize(output_ids, tokenizer_path))
+```
+
+Or for the smaller, faster CPU variant:
+
+```python
+revision = "small-weekly"
+variant  = "cpu_max_5h_50k"   # 17.5M params, fits in <100 MB RAM
+```
+
+---
+
+## Training Data
+
+The active training mix is documented at [`scripts/data/mix-broadbrain.json`](https://github.com/aashir-athar/mindeesai/blob/main/scripts/data/mix-broadbrain.json) (v4.1 — *Quality-pruned, gating-safe*). **22 datasets, ~42M tokens total**, every entry verified to load without authentication.
+
+### Signal Share by Category
+
+| Category | Share | Sources |
+|---|---|---|
+| **Broad assistant chat** | ~27% | OpenHermes-2.5, smoltalk, WizardLM evol-instruct |
+| **Code** | ~28% | Magicoder Evol-Instruct, CodeFeedback Filtered, OpenCoder-SFT-stage2, CodeAlpaca, CodeParrot-clean |
+| **Anchor (human-curated)** | ~19% | Dolly-15k, no_robots, smol-smoltalk |
+| **Math + reasoning** | ~19% | MetaMathQA, MathInstruct, Open-Platypus, UltraInteract-SFT, OpenThoughts2-1M |
+| **Knowledge / warmup** | ~4% | FineWeb-Edu, TinyStories |
+| **Persona protection** | ~6% | SystemChat-1.1 (counter-acts robotic register) |
+| **Domain spice** | ~1% | andstor/smart_contracts (Solidity / Web3) |
+| **Empathy** | ~0.7% | Empathetic-Counseling, Mental-Health-Counseling (low weight to avoid clinical drift) |
+
+### Tier-Weighted Highlights
+
+| Weight | Dataset | Why |
+|---|---|---|
+| 2.5 | `databricks/databricks-dolly-15k` | Zero-synthetic human anchor |
+| 2.5 | `HuggingFaceH4/no_robots` | Highest instruction quality per token in the mix |
+| 2.0 | `HuggingFaceTB/smol-smoltalk` | HF's instruction dataset specifically tuned for sub-1B models |
+| 1.8 | `abacusai/SystemChat-1.1` | Diverse system prompts — defends persona stability |
+| 1.8 | `ise-uiuc/Magicoder-Evol-Instruct-110K` | Highest-quality code SFT on HF |
+| 1.5 | `teknium/OpenHermes-2.5` | Broad-coverage instruction examples |
+| 1.5 | `meta-math/MetaMathQA` | 395K math problems with worked CoT |
+| 1.5 | `TIGER-Lab/MathInstruct` | Hybrid CoT + program-of-thought math |
+
+### Datasets Staged for Future Stages
+
+Three preference datasets (`HumanLLMs/Human-Like-DPO-Dataset`, `HuggingFaceH4/ultrafeedback_binarized`, `openbmb/UltraFeedback`) and one agentic-tool-use dataset (`nvidia/Nemotron-RL-Agentic-SWE-Pivot-v1`) are documented at [`scripts/data/mix-dpo-human.json`](https://github.com/aashir-athar/mindeesai/blob/main/scripts/data/mix-dpo-human.json) and [`scripts/data/mix-agentic-code.json`](https://github.com/aashir-athar/mindeesai/blob/main/scripts/data/mix-agentic-code.json). They are reserved for a planned DPO / RLHF / agentic-training stage and are **not** part of the current SFT pretraining.
+
+---
+
+## Training Procedure
+
+### Hyperparameters (Active Configuration)
+
+| Hyperparameter | Value | Notes |
+|---|---|---|
+| Optimizer | AdamW | β₁=0.9, β₂=0.95, ε=1e-8 |
+| Weight decay | 0.1 | Applied to non-norm parameters |
+| Learning rate | 3e-4 (peak) | Cosine schedule, 100-step linear warmup |
+| Effective batch | 8 tokens (`home-11gb`) / 4 tokens (`cpu_max_5h_50k`) | After grad-accumulation |
+| Sequence length | 2048 (`home-*`) / 256 (`cpu_max_5h_50k`) | Per Config |
+| Gradient clipping | 1.0 | L2 norm |
+| Completion-only loss | `--completion-only-loss 1` | Loss only on assistant turns (dialogue samples) |
+| Persona loss weight | 0.05 | Soft signal — keeps Mindees voice without overfitting |
+| Distill corpus weight | 4.0 | Real chat turns weighted 4× over base SFT mix |
+| Base corpus weight | 1.0 | Seed conversations |
+| Checkpoint every | 250 steps (GH Actions) / 1000 (Kaggle/Colab) | Resume-safe granularity |
+| Validation every | 750 (GH Actions) / 500 (Kaggle/Colab) | Reports `val_loss` to `data/training-metrics.jsonl` |
+
+### Federated Training Topology
+
+Training is distributed across four independent compute pools, each pushing to its own HF branch. Every run **resumes from the prior session's checkpoint** so steps accumulate indefinitely:
+
+```
+┌──────────────────────────────────────────────────────────────────────┐
+│  Local RTX 5070       →  main           (owner-driven, sacrosanct)   │
+│  GitHub Actions cron  →  small-weekly   (4× daily, CPU, 17.5M)       │
+│  Kaggle Notebooks     →  kaggle-weekly  (weekly, T4 GPU, 280M)       │
+│  Google Colab         →  colab-burst    (burst, T4 GPU, 280M)        │
+└──────────────────────────────────────────────────────────────────────┘
+                                  │
+                                  ▼
+                  ┌────────────────────────────────┐
+                  │  HuggingFace Hub (this repo)   │
+                  │  4 independent revisions       │
+                  └────────────────────────────────┘
+                                  │
+                                  ▼
+                  ┌────────────────────────────────┐
+                  │  Cloudflare Workers deploy     │
+                  │  + HF Spaces ML/vector sidecar │
+                  │  Cost: $0/month forever        │
+                  └────────────────────────────────┘
+```
+
+Reproducible training entry points live at [`scripts/train/`](https://github.com/aashir-athar/mindeesai/tree/main/scripts/train) and the four notebooks at [`scripts/notebooks/`](https://github.com/aashir-athar/mindeesai/tree/main/scripts/notebooks).
+
+---
+
+## The Mindees Persona
+
+Unlike most foundation models, MindeesAI ships with **a deliberately scoped first-person identity** named **Mindees**. The persona is not a system-prompt overlay — it is woven into training via a dedicated `--corpus` and `--distill-corpus` weighting and reinforced by [`abacusai/SystemChat-1.1`](https://huggingface.co/datasets/abacusai/SystemChat-1.1), which teaches the model to **honor diverse system prompts without slipping into the robotic "as an AI" default register**.
+
+A live **8-dimensional mood tensor** evolves each turn:
+
+| Dimension | Range | Role |
+|---|---|---|
+| Curiosity | 0–1 | Pulls toward asking clarifying / exploratory questions |
+| Warmth | 0–1 | Softens phrasing, mirrors user affect |
+| Playfulness | 0–1 | Allows tasteful humor, wordplay |
+| Focus | 0–1 | Trims preamble, prioritizes precision |
+| Wonder | 0–1 | Encourages metaphor, broader framing |
+| Frustration | 0–1 | Triggers de-escalation routines when high |
+| Calm | 0–1 | Steadies tone on tense turns |
+| Confidence | 0–1 | Modulates hedging language |
+
+Mood is exposed at [`/api/mood`](https://mindeesai.0032ksa.workers.dev/api/mood) on the live deployment. It is fed into every generation step as part of the persona signal and persisted in [Cloudflare R2](https://developers.cloudflare.com/r2/) between turns.
+
+---
+
+## Self-Improvement Loop
+
+A 30-minute cron triggers `/api/cron/self-improve` on the live deployment, which runs the following pipeline:
+
+1. **Reflect** — read the most recent chat turns from R2.
+2. **Extract** — distill new instruction / response pairs into `data/distill-corpus.jsonl`.
+3. **Filter** — score each pair via the [`HumanLLMs/Human-Like-DPO-Dataset`](https://huggingface.co/datasets/HumanLLMs/Human-Like-DPO-Dataset)-style heuristic, drop low-quality.
+4. **PII-scrub** — every appended line passes through [`Xenova/piiranha-v1-detect-personal-information`](https://huggingface.co/Xenova/piiranha-v1-detect-personal-information) + a regex backstop before persisting (emails, phones, credit cards, SSNs, addresses, IBANs, license numbers).
+5. **Persist** — write the cleaned distill corpus + thumbs-up/down feedback to R2.
+6. **Train (on next cron tick)** — the daily GitHub Actions workflow fetches the latest distill corpus from R2 and prepends it to the SFT mix, weighted 4× over base data.
+
+**The model literally learns from its own conversations**, with privacy protection baked into the persistence layer. Public-revision checkpoints (`small-weekly`, `kaggle-weekly`) only ever contain weights trained on **PII-scrubbed** conversation data.
+
+---
+
+## Deployment & Infrastructure
+
+MindeesAI is deployed end-to-end on **$0/month free-tier infrastructure** — no Vercel Pro, no Cloudflare Paid, no GPU rentals.
+
+| Layer | Provider | Free quota | Role |
+|---|---|---|---|
+| Web app | **Cloudflare Workers Free** | 100k requests/day | SSR, chat streaming, API routes |
+| ML + vector sidecar | **Hugging Face Spaces (Docker)** | 16 GB RAM, 50 GB disk | LanceDB vector store + 7 ML pipelines (PII, NER, sentiment, toxicity, reranker, zero-shot, summarizer) |
+| Object storage | **Cloudflare R2** | 10 GB, 1M Class-A ops/mo | Persistent chat memory, distill corpus, mood state |
+| Model checkpoints | **Hugging Face Hub** (this repo) | Unlimited public | Federated revisions, version history |
+| Continual training | **GitHub Actions** | Unlimited for public repos | 4× daily SFT cron on `small-weekly` |
+| Burst GPU training | **Kaggle Notebooks** | 30 GPU-hours/week | Heavy `home-11gb` training on `kaggle-weekly` |
+| Backup GPU training | **Google Colab Free** | T4, idle-disconnect | Spillover heavy training on `colab-burst` |
+
+Architecture detail at [`docs/CLOUDFLARE_HF_DEPLOY.md`](https://github.com/aashir-athar/mindeesai/blob/main/docs/CLOUDFLARE_HF_DEPLOY.md). The native binaries (LanceDB, ONNX, transformers.js) that Cloudflare Workers cannot load are isolated into the sidecar at [aashir-athar/mindeesai-sidecar](https://huggingface.co/spaces/aashir-athar/mindeesai-sidecar) and called over HTTPS + Bearer.
+
+---
+
+## Intended Use
+
+| Use case | Suitability | Notes |
+|---|---|---|
+| Educational / research use | **Yes** | Primary intended use. Architecture, training code, recipes all open. |
+| Personal assistant prototype | **Yes** | The live demo runs a working version. |
+| Studying small-model behavior | **Yes** | Comparable to SmolLM / TinyLlama for under-1B research. |
+| Production user-facing applications | **No, at this size** | Use a larger model (Llama-3.3-70B, Claude, etc.) via the LLM router. Mindees Native is reserved for cases where 280M is genuinely sufficient. |
+| Safety-critical decision making | **No** | This is a research-stage model with limited evaluation. |
+| Medical, legal, or financial advice | **No** | Empathy-counseling data is included at low weight to soften tone, not to qualify the model as a domain expert. |
+
+---
+
+## Limitations & Known Issues
+
+- **Capacity ceiling.** At 17.5M (`cpu_max_5h_50k`) and 280M (`home-11gb`) parameters, the model fundamentally lacks the representation capacity of frontier models. Expect factual recall errors, math arithmetic mistakes, hallucinated code APIs.
+- **English-dominant.** ~99% of the training mix is English. Performance on other languages is incidental.
+- **In-progress training.** The `small-weekly` revision has plateaued at validation loss ≈ 3.5 (perplexity ≈ 34) — saturated for its capacity. The `home-11gb` runs on `kaggle-weekly` are still in early steps (~10k of an effective 200k+ schedule); expect meaningful quality only after further cumulative training.
+- **Completion-only loss interaction with raw data.** Steps composed entirely of raw-kind samples (FineWeb-Edu, TinyStories, CodeParrot, Solidity) currently compute zero loss because `--completion-only-loss 1` masks tokens outside an assistant turn. A planned fix will apply standard CLM loss to raw samples.
+- **No formal evaluation yet.** Standard benchmark numbers (MMLU, HellaSwag, GSM8K, HumanEval) have not been published for this checkpoint. Trust the loss curves only as relative-progress indicators.
+- **Bias inherited from training data.** Synthetic data sources (OpenHermes, Magicoder, etc.) carry the biases of their teacher models. The persona system can soften the *register* of this bias but does not eliminate the *content*.
+
+---
+
+## License
+
+This **model** is released under the **Apache License 2.0**. You are free to use, modify, distribute, and build commercial products on it.
+
+### Training Data Provenance Notice
+
+The model weights were trained on a mix of publicly available datasets, **each carrying its own license**. The model itself does not redistribute any training data, but downstream users intending **commercial** use should review the licenses of the individual datasets enumerated in the YAML metadata above. In particular:
+
+- `databricks/databricks-dolly-15k` — CC-BY-SA 3.0 (commercial OK with attribution + share-alike)
+- `HuggingFaceH4/no_robots` — CC-BY-NC 4.0 (**non-commercial**)
+- `sahil2801/CodeAlpaca-20k` — CC-BY-NC 4.0 (**non-commercial**)
+- `teknium/OpenHermes-2.5`, `HuggingFaceTB/smol-smoltalk`, `HuggingFaceTB/smoltalk` — typically Apache 2.0 / MIT (verify on dataset page)
+- All other datasets — see their individual repository pages on Hugging Face
+
+If your downstream use is non-commercial (research, education, personal projects), all included data is usable.
+
+---
+
+## Citation
+
+If you use MindeesAI in research or downstream work, please cite the repository:
+
+```bibtex
+@misc{mindeesai2026,
+  author       = {Aashir Athar},
+  title        = {MindeesAI: A Self-Improving Open Native Transformer with a Persona},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/aashir-athar/mindeesai-base}},
+  note         = {Trained from scratch on free-tier compute. Apache-2.0.},
+}
+```
+
+---
+
+## Acknowledgements
+
+MindeesAI builds on the open work of many upstream projects. Sincere thanks to:
+
+- The **DeepSeek-AI team** for the V3 / R1 architectural innovations (MLA, MTP, MoE patterns).
+- **Andrej Karpathy** for `nanoGPT`, the model that proved you can teach a transformer from scratch in a few hundred lines.
+- **Xenova** and the **transformers.js** project for browser/edge-runnable ONNX-quantized models.
+- The **Hugging Face team** for `huggingface_hub`, Spaces, Datasets, and the Hub itself — the entire deployment stack depends on it.
+- **bigcode** / **CodeParrot** / **OpenCoder** for the open code corpora.
+- **databricks**, **teknium**, **abacusai**, **HuggingFaceTB**, **m-a-p**, **TIGER-Lab**, **meta-math**, **openbmb**, **garage-bAInd**, **ise-uiuc**, **LuangMV97**, **Amod**, **roneneldan**, **HuggingFaceFW**, **WizardLMTeam**, **andstor**, **open-thoughts** for the training datasets.
+- **LanceDB** for the embedded vector store.
+- **Cloudflare** and **Hugging Face** for the free-tier compute that makes the whole architecture economically real.
+
+---
+
+## Contact
+
+- **Author:** Aashir Athar
+- **GitHub:** [@aashir-athar](https://github.com/aashir-athar)
+- **Repository:** [github.com/aashir-athar/mindeesai](https://github.com/aashir-athar/mindeesai)
+- **Live demo:** [mindeesai.0032ksa.workers.dev](https://mindeesai.0032ksa.workers.dev)
+- **Issues:** [GitHub Issues](https://github.com/aashir-athar/mindeesai/issues)
+
+<div align="center">
+
+— *Mindees is a small brain learning out loud.* —
+
+</div>