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LICENSE

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+Apache License
+Version 2.0, January 2004
+http://www.apache.org/licenses/
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.

README.md

@@ -1,59 +1,119 @@
 ---
-license: mit
 language:
-- en
-pipeline_tag: text-generation
-library_name: transformers.js
+  - en
+license: apache-2.0
 tags:
-- text-generation-inference
-- distillation
-- grpo
-- vae
-- pytorch
-- agent
-- education
-- SLM
-- small
-- tiny
-- smol
-- distilled
-- micro
-- study
-- testing
-- blackbox
-- offline
-- localdb
+  - micro-distillery
+  - bigram
+  - grpo
+  - vae
+  - distilled
+  - smollm2
+  - qwen2.5
+  - olmo
+  - tinyllama
+library_name: micro-distillery
 ---
 
-<div style="
-    background: #00FF00;
-    border-left: 4px solid #00FF00;
-    padding: 1.5rem;
-    margin: 2rem 0;
-    font-family: 'Fira Code', 'Courier New', monospace;
-    color: #00FF00;
-    border-radius: 0 8px 8px 0;
-">
-    <pre style="
-        font-size: 8px;
-        line-height: 1.2;
-        margin: 0;
-        overflow-x: auto;
-        color: #00FF00;
-    ">
-      :::   :::   ::::::::::: ::::::::  :::::::::   ::::::::  ::::::::: 
-    :+:+: :+:+:      :+:    :+:    :+: :+:    :+: :+:    :+: :+:    :+: 
-   +:+ +:+:+ +:+     +:+    +:+        +:+    +:+ +:+    +:+ |:|    +:+  
-   +#+  +:+  +#+     +#+    +#+        +#++:++#:  +#+    +:+ |#|    +:+   
-   +#+       +#+     +#+    +#+        +#+    +#+ +#+    +#+ |#|    +#+    
-   ###       ###     ###    ###    ### ###    ### ###    ### ###    ###
-   ###       ### ########### ########  ###   ###   ########  #########
-</div>
-
-# MICROD ALPHA VERSION UNDER TESTING AND DEVELOPMENT: 
-This model was made with the 'Micro Distillery' 
-
-
-  
-## Model Description
-UNDER DEVELOPMENT: MAJOR UPDATE UI FIX
+# Micro Distillery — Bigram Distilled Model
+
+This model was trained from scratch using a combination of:
+
+- **GRPO** (Group Relative Policy Optimization) for character-level trajectory training
+- **VAE filtering** to keep only high-quality trajectories
+- **4x quantum harmonic oscillator** superposition for deterministic seeding
+
+It is a compact character-level bigram model - no transformer weights,
+just transition probabilities. Runs on any machine with Python 3.8+.
+
+## Reference Architectures
+
+The bundle metadata references these modern (2025-2026) open models as
+inspiration sources. The actual model is a distilled bigram - not a
+transformer checkpoint - and does not load via `AutoModelForCausalLM`.
+
+- [SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) (HuggingFaceTB)
+- [Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) (Qwen)
+- [OLMo-2-1B](https://huggingface.co/allenai/OLMo-2-0425-1B) (AllenAI)
+- [TinyLlama-1.1B](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) (TinyLlama)
+
+## Model Details
+
+- **Format**: micro-distill-bigram-v1
+- **Version**: 3.1.1
+- **Reference arch**: tiny-bigram
+- **Vocab size**: 27 tokens
+- **Transition states**: 27
+- **Harmonic seed**: 1251963776
+- **Created**: 2026-10-23T14:02:10.625Z
+
+## Quick Start
+
+```bash
+# 1. Extract the bundle
+unzip micro-distillery-*.zip
+cd micro-distillery-*
+
+# 2. Run the test harness
+chmod +x test.sh run.sh
+./test.sh
+
+# 3. Or chat directly
+python3 test.py
+```
+
+## Files in the Bundle
+
+| File | Description |
+|------|-------------|
+| `model.json` | The trained bigram model (all weights) |
+| `config.json` | HuggingFace-style metadata config |
+| `vocab.json` | Character vocabulary |
+| `tokenizer_config.json` | Tokenizer metadata |
+| `knowledge_graph.json` | 2D graph used to seed the model |
+| `training_logs.json` | Metrics + quantum measurements |
+| `test.py` | Interactive chat wrapper |
+| `test.sh` | Test harness |
+| `test_readme.md` | Testing instructions |
+| `run.sh` | Minimal launcher |
+| `to_parquet.py` | Convert to parquet |
+| `diagrams/pipeline.svg` | Training pipeline diagram |
+| `diagrams/architecture.svg` | Model architecture diagram |
+
+## Training Pipeline
+
+1. **Fetch** - pulls corpora and model cards from HuggingFace + GitHub (2026 open sources)
+2. **Knowledge graph** - builds a character-transition graph, laid out in 2D
+3. **Seed** - initializes bigram transition counts from the corpus
+4. **GRPO** - samples trajectories, scores with a name heuristic, boosts high-advantage transitions
+5. **VAE filter** - retains only high-probability trajectories
+6. **Quantum seed** - 4 harmonic oscillators (w = 1, phi, 2, pi) collapse to a deterministic seed
+7. **Export** - produces a HuggingFace-ready .zip bundle
+
+## Uploading to Hugging Face
+
+The bundle is structured so you can upload it directly to a HF repo:
+
+```bash
+pip install huggingface_hub
+huggingface-cli login
+huggingface-cli upload your-username/micro-distillery-bigram . --repo-type model
+```
+
+The `model.json` file is not a transformer checkpoint - use `test.py` to run it.
+`config.json` is provided for HF metadata and Datasets viewer compatibility.
+
+## Data Sources
+
+This bundle was produced by fetching from these free, open endpoints:
+
+- `raw.githubusercontent.com/karpathy/makemore/master/names.txt` (training corpus)
+- `huggingface.co/api/models?search=smollm2` (model reference list)
+- `huggingface.co/api/models?search=qwen2.5-0.5b` (model reference list)
+- `huggingface.co/HuggingFaceTB/SmolLM2-135M/raw/main/README.md` (model card)
+- `huggingface.co/allenai/OLMo-2-0425-1B/raw/main/README.md` (model card)
+- `api.github.com/repos/karpathy/makemore` (repo metadata)
+
+## License
+
+Apache 2.0

config.json

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+{
+  "architectures": [
+    "MicroDistilleryBigramForCausalLM"
+  ],
+  "model_type": "custom",
+  "custom_model_type": "micro-distillery-bigram",
+  "vocab_size": 256,
+  "max_position_embeddings": 1024,
+  "hidden_size": 512,
+  "num_hidden_layers": 0,
+  "num_attention_heads": 0,
+  "intermediate_size": 0,
+  "activation_function": "gelu_new",
+  "layer_norm_epsilon": 0.00001,
+  "use_cache": true,
+  "bos_token_id": 0,
+  "eos_token_id": 1,
+  "transformers_version": "4.44.0",
+  "library_name": "micro-distillery",
+  "micro_distillery": {
+    "format": "bigram-distilled",
+    "dialect": "character-level",
+    "reference_arch": "tiny-bigram",
+    "distill_from": [
+      "smollm2-135m",
+      "qwen2.5-0.5b",
+      "olmo2-1b",
+      "tinyllama-1.1b"
+    ],
+    "harmonic_seed": 1251963776,
+    "harmonic_frequencies": [
+      1,
+      1.618033988749895,
+      2,
+      3.141592653589793
+    ],
+    "grpo_config": {
+      "group_size": 8,
+      "kl_penalty": 0.1
+    },
+    "vae_config": {
+      "latent_dim": 32,
+      "filter_threshold": 0.7
+    }
+  }
+}

model.json

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+{
+  "format": "micro-distill-bigram-v1",
+  "version": "3.1.1",
+  "arch": "tiny-bigram",
+  "created": "2026-10-23T14:02:10.625Z",
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "vocab": [
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+  "harmonic_frequencies": [
+    1,
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+    2,
+    3.141592653589793
+  ],
+  "config": {
+    "grpo": {
+      "group_size": 8,
+      "kl_penalty": 0.1
+    },
+    "vae": {
+      "latent_dim": 32,
+      "filter_threshold": 0.7
+    },
+    "policy_arch": "tiny-bigram"
+  },
+  "metrics": {
+    "training_steps": 60,
+    "final_loss": 0.010978239385384714,
+    "average_reward": 0.6262606837606838
+  }
+}

requirements.txt

@@ -0,0 +1,4 @@
+# Core test.py requires only Python 3.8+ standard library.
+# Optional:
+pandas>=2.0
+pyarrow>=14.0

run.sh

@@ -0,0 +1,10 @@
+#!/usr/bin/env bash
+# run.sh - Minimal launcher for test.py.
+set -euo pipefail
+cd "$(dirname "$0")"
+PY="${PYTHON:-python3}"
+if ! command -v "$PY" >/dev/null 2>&1; then
+  echo "Error: python3 not found." >&2
+  exit 1
+fi
+exec "$PY" test.py "$@"

test.py

@@ -0,0 +1,120 @@
+#!/usr/bin/env python3
+"""
+test.py - Micro Distillery model chat wrapper.
+
+Loads model.json (a GRPO+VAE-distilled bigram model) and provides
+a simple REPL chat interface.
+
+Usage:
+    python3 test.py                      # interactive chat
+    python3 test.py hello                # one-shot from prompt
+    python3 test.py --temperature 1.2    # one-shot with custom temperature
+    python3 test.py                      # (interactive also supports --temperature)
+"""
+import json
+import os
+import random
+import sys
+
+MODEL_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "model.json")
+
+
+def load_model(path=MODEL_PATH):
+    with open(path, "r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def sample_next(transitions, token, rng, temperature=1.0):
+    bucket = transitions.get(token)
+    if not bucket:
+        return None
+    tokens = list(bucket.keys())
+    inv_t = 1.0 / max(temperature, 1e-3)
+    weights = [max(bucket[t], 1e-12) ** inv_t for t in tokens]
+    total = sum(weights)
+    if total <= 0:
+        return None
+    r = rng.random() * total
+    acc = 0.0
+    for t, w in zip(tokens, weights):
+        acc += w
+        if r <= acc:
+            return t
+    return tokens[-1]
+
+
+def generate(model, prompt, max_tokens=32, temperature=0.9):
+    transitions = model.get("transitions", {})
+    bos = model.get("bos_token", "<|endoftext|>")
+    eos = model.get("eos_token", "<|endoftext|>")
+    seed = int(model.get("harmonic_seed", 1337)) & 0xFFFFFFFF
+    rng = random.Random(seed)
+
+    if prompt and prompt.strip():
+        current = prompt.strip()[-1]
+        prefix = prompt.strip()
+    else:
+        current = bos
+        prefix = ""
+
+    generated = ""
+    for _ in range(max_tokens):
+        nxt = sample_next(transitions, current, rng, temperature=temperature)
+        if nxt is None or nxt == eos:
+            break
+        generated += nxt
+        current = nxt
+    return prefix + generated
+
+
+def main(temperature=0.9):
+    print("=== Micro Distillery Model Chat ===")
+    if not os.path.exists(MODEL_PATH):
+        print("Error: " + MODEL_PATH + " not found.", file=sys.stderr)
+        sys.exit(1)
+    model = load_model()
+    print("Format:      " + str(model.get("format")))
+    print("Vocab size:  " + str(len(model.get("vocab", []))))
+    print("Transitions: " + str(len(model.get("transitions", {}))))
+    print("Seed:        " + str(model.get("harmonic_seed")))
+    print("Temperature: " + str(temperature))
+    print("Type /quit to exit.\n")
+    while True:
+        try:
+            prompt = input("You: ").strip()
+        except (EOFError, KeyboardInterrupt):
+            print("\nBye!")
+            break
+        if not prompt:
+            continue
+        if prompt.lower() in ("/quit", "/exit", "quit", "exit"):
+            break
+        out = generate(model, prompt, temperature=temperature)
+        print("Bot: " + out)
+
+
+if __name__ == "__main__":
+    args = sys.argv[1:]
+    temperature = 0.9
+    prompts = []
+    i = 0
+    while i < len(args):
+        if args[i] == "--temperature" and i + 1 < len(args):
+            try:
+                temperature = float(args[i + 1])
+            except ValueError:
+                print("Warning: invalid --temperature value, using 0.9", file=sys.stderr)
+            i += 2
+        elif args[i] in ("--help", "-h"):
+            print("Usage: python3 test.py [prompt...] [--temperature T]")
+            sys.exit(0)
+        elif args[i].startswith("--"):
+            i += 1
+        else:
+            prompts.append(args[i])
+            i += 1
+    if prompts:
+        model = load_model()
+        print(generate(model, " ".join(prompts), temperature=temperature))
+    else:
+        main(temperature=temperature)

test.sh

@@ -0,0 +1,45 @@
+#!/usr/bin/env bash
+# test.sh - Test harness for the exported Micro Distillery model.
+set -euo pipefail
+cd "$(dirname "$0")"
+
+PY="${PYTHON:-python3}"
+
+echo "================================================"
+echo " Micro Distillery - Model Test Harness"
+echo "================================================"
+echo ""
+
+if ! command -v "$PY" >/dev/null 2>&1; then
+  echo "ERROR: $PY not found. Install Python 3.8+." >&2
+  echo "  macOS:   brew install python3" >&2
+  echo "  Ubuntu:  sudo apt install python3" >&2
+  echo "  Windows: https://python.org/downloads/" >&2
+  exit 1
+fi
+
+echo "[1/3] Python: $($PY --version 2>&1)"
+
+if [ ! -f model.json ]; then
+  echo "ERROR: model.json not found in $(pwd)" >&2
+  exit 1
+fi
+echo "[2/3] Found model.json"
+
+$PY -c "
+import json
+m = json.load(open('model.json'))
+assert 'transitions' in m, 'missing transitions'
+assert 'vocab' in m, 'missing vocab'
+print('OK: format=%s vocab=%d transitions=%d' % (m.get('format','?'), len(m['vocab']), len(m['transitions'])))
+"
+echo "[3/3] Model validation passed"
+echo ""
+echo "=== Sample Generations ==="
+for prompt in alice bob zoe liam; do
+  echo -n "  $prompt -> "
+  $PY test.py "$prompt" 2>/dev/null | tail -1 || echo "(failed)"
+done
+echo ""
+echo "=== Interactive Chat (type /quit to exit) ==="
+exec $PY test.py

test_readme.md

@@ -0,0 +1,56 @@
+# Testing Your Exported Model
+
+This bundle contains a fully runnable bigram language model. No GPU,
+no external dependencies - Python 3.8+ standard library only.
+
+## Files
+
+| File | Purpose |
+|------|---------|
+| `model.json` | The trained model (transitions + config + metrics) |
+| `test.py` | Interactive chat wrapper |
+| `test.sh` | Test harness (validates + generates samples) |
+| `run.sh` | Minimal launcher for test.py |
+
+## Quick Start
+
+### macOS / Linux
+
+```bash
+chmod +x test.sh run.sh
+./test.sh
+```
+
+### Windows (PowerShell)
+
+```powershell
+python test.py
+```
+
+### One-shot generation
+
+```bash
+python3 test.py alice
+# => alicea
+```
+
+### Custom temperature
+
+```bash
+python3 test.py --temperature 0.5
+python3 test.py alice --temperature 1.2
+```
+
+## Manual Test
+
+```bash
+python3 -c "import json; m=json.load(open('model.json')); print(len(m['vocab']), 'tokens,', len(m['transitions']), 'states')"
+```
+
+Expected output: `<N> tokens, <M> states`.
+
+## Troubleshooting
+
+- **`python3 not found`** - install Python 3.8+ from https://python.org
+- **`model.json not found`** - make sure you are inside the extracted directory
+- **`Permission denied`** - run `chmod +x test.sh run.sh` first

to_parquet.py

@@ -0,0 +1,50 @@
+#!/usr/bin/env python3
+"""Convert model.json to model.parquet.
+
+Requires: pandas, pyarrow
+    pip install pandas pyarrow
+"""
+import json
+import os
+import sys
+
+MODEL_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "model.json")
+OUT_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "model.parquet")
+
+
+def main():
+    try:
+        import pandas as pd
+    except ImportError:
+        print("Error: pandas required. Install: pip install pandas pyarrow", file=sys.stderr)
+        sys.exit(1)
+
+    with open(MODEL_PATH, "r", encoding="utf-8") as f:
+        model = json.load(f)
+
+    rows = []
+    for state, bucket in model.get("transitions", {}).items():
+        for nxt, prob in bucket.items():
+            rows.append({
+                "state": state,
+                "next_token": nxt,
+                "probability": float(prob),
+                "harmonic_seed": int(model.get("harmonic_seed", 0)),
+                "format": model.get("format", "unknown"),
+                "version": model.get("version", "0.0"),
+            })
+
+    if not rows:
+        print("Warning: no transitions. Writing metadata-only parquet.")
+        rows = [{"state": "", "next_token": "", "probability": 0.0,
+                 "harmonic_seed": int(model.get("harmonic_seed", 0)),
+                 "format": model.get("format", "unknown"),
+                 "version": model.get("version", "0.0")}]
+
+    df = pd.DataFrame(rows)
+    df.to_parquet(OUT_PATH, index=False)
+    print("Wrote " + str(len(df)) + " rows to " + OUT_PATH)
+
+
+if __name__ == "__main__":
+    main()

tokenizer_config.json

@@ -0,0 +1,10 @@
+{
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "unk_token": "<|endoftext|>",
+  "pad_token": "<|endoftext|>",
+  "add_prefix_space": false,
+  "model_max_length": 1024,
+  "name_or_path": "micro-distillery"
+}

training_logs.json

@@ -0,0 +1,122 @@
+{
+  "created": "2026-10-23T14:02:10.625Z",
+  "metrics": {
+    "trainingSteps": 60,
+    "finalLoss": 0.010978239385384714,
+    "averageReward": 0.6262606837606838,
+    "cacheHits": 1,
+    "cacheMisses": 251,
+    "filteredCount": 0,
+    "totalSamples": 0,
+    "vaeLoss": 0
+  },
+  "quantum_measurements": [
+    {
+      "oscillator": 0,
+      "energy": 0.5,
+      "x": 0.615234375,
+      "t": 0.1,
+      "probabilities": [
+        0.25,
+        0.25,
+        0.25,
+        0.25
+      ]
+    },
+    {
+      "oscillator": 3,
+      "energy": 10.995574287564276,
+      "x": -1.1074424442685453,
+      "t": 0.2,
+      "probabilities": [
+        0.25,
+        0.25,
+        0.25,
+        0.25
+      ]
+    },
+    {
+      "oscillator": 3,
+      "energy": 10.995574287564276,
+      "x": 0.23140588387700944,
+      "t": 0.30000000000000004,
+      "probabilities": [
+        0.24999999999999997,
+        0.25,
+        0.24999999999999997,
+        0.25
+      ]
+    },
+    {
+      "oscillator": 1,
+      "energy": 2.4270509831248424,
+      "x": -0.7830804739380586,
+      "t": 0.4,
+      "probabilities": [
+        0.25,
+        0.25,
+        0.25,
+        0.24999999999999997
+      ]
+    },
+    {
+      "oscillator": 3,
+      "energy": 10.995574287564276,
+      "x": -0.2644638672880113,
+      "t": 0.5,
+      "probabilities": [
+        0.24999999999999997,
+        0.25,
+        0.25,
+        0.25
+      ]
+    },
+    {
+      "oscillator": 3,
+      "energy": 10.995574287564276,
+      "x": 1.7355441290775708,
+      "t": 0.6,
+      "probabilities": [
+        0.25,
+        0.25,
+        0.25,
+        0.25
+      ]
+    },
+    {
+      "oscillator": 0,
+      "energy": 0.5,
+      "x": 1.171875,
+      "t": 0.7,
+      "probabilities": [
+        0.25,
+        0.25,
+        0.25,
+        0.25
+      ]
+    },
+    {
+      "oscillator": 3,
+      "energy": 10.995574287564276,
+      "x": -0.34710882581551417,
+      "t": 0.7999999999999999,
+      "probabilities": [
+        0.25,
+        0.25,
+        0.25,
+        0.25
+      ]
+    }
+  ],
+  "config": {
+    "grpo": {
+      "group_size": 8,
+      "kl_penalty": 0.1
+    },
+    "vae": {
+      "latent_dim": 32,
+      "filter_threshold": 0.7
+    },
+    "policy_arch": "tiny-bigram"
+  }
+}

vocab.json

@@ -0,0 +1,29 @@
+{
+  "e": 0,
+  "m": 1,
+  "a": 2,
+  "<|endoftext|>": 3,
+  "o": 4,
+  "l": 5,
+  "i": 6,
+  "v": 7,
+  "s": 8,
+  "b": 9,
+  "p": 10,
+  "h": 11,
+  "c": 12,
+  "r": 13,
+  "t": 14,
+  "y": 15,
+  "n": 16,
+  "g": 17,
+  "z": 18,
+  "f": 19,
+  "d": 20,
+  "u": 21,
+  "k": 22,
+  "w": 23,
+  "q": 24,
+  "x": 25,
+  "j": 26
+}