TopoGPT3

A 24.5M parameter complex-valued autoregressive language model for code, instrumented with spectral and geometric diagnostics over training dynamics.

This repository contains the model definition, the curriculum trainer, and two inference engines: a standard sampler and a hierarchical recursive reasoning sampler (HRM) that requires no extra trained parameters.

The work is documented in detail in topogpt3.md.

Documentation

• Quick Start — Get running in under five minutes.
• Tutorial — Step-by-step guide from installation to custom training.
• Essential Concepts — Core ideas behind complex-valued spectral operators, Grassmannian diagnostics, and HRM.
• Command Cheatsheet — Quick reference for CLI commands and Python API.
• Comparison — How TopoGPT3 relates to similar small-scale and code-focused models.
• Claude Integration Guide — Using TopoGPT3 with Anthropic models and hybrid pipelines.
• Technical Paper — Full experimental write-up and results.

Motivation

Most code language models scale through size. TopoGPT3 explores the opposite direction: whether better representations can let a much smaller model learn programming structure efficiently. Source code carries strong internal structure (recursion, composition, scope, repeated motifs), and complex-valued parameters may encode phase relationships that capture this structure more compactly than real-valued weights of equal count.

Checkpoints

https://huggingface.co/grisiscomeback/TopoGPT3/tree/main/checkpoints_topogpt3/last

Architecture summary

• Autoregressive transformer with complex-valued spectral operators.
• Quaternion-inspired layers for parameter efficiency.
• A Gauss-style optimization for complex multiplication: three real multiplications per contraction instead of four.
• Sliding window attention (ATTN_WINDOW) with configurable window size to control KV-cache memory footprint in long sequences. Set to 0 for full attention or a positive integer for local attention with O(W * S) complexity.
• Latent memory tokens (N_MEMORY_TOKENS) for fixed-size context compression across long sequence segments.
• Approximately 24.5M parameters at the default small scale.

The base architecture lives in topogpt3/model.py. The curriculum trainer and the Grassmannian / Fisher / phase diagnostics live in topogpt3/train.py.

Training pipeline

Training proceeds through a four-tier curriculum, from short instructions to real multilingual code:

1. CodeAlpaca
2. Code Feedback (filtered instruction)
3. Magicoder Evol Instruct
4. Tiny subset of The Stack

Each tier maintains disjoint train, validation and holdout splits. The holdout is never used during training; it is reserved to measure true generalization at the end of each tier and at the end of the full pipeline.

Mixed precision is used on a single GPU. Checkpoints are written atomically to checkpoints_topogpt3/last/ as safetensors plus an optimizer file and a JSON state. Older step_* directories are still loadable for backwards compatibility.

Optimization diagnostics

At regular intervals the trainer extracts the kernel tensor, performs a truncated SVD on the leading 16 modes, normalizes them, and records:

• accumulated phase between consecutive normalized dominant kernels,
• net angular drift W (a winding-like proxy),
• empirical Fisher spectral gap Delta_F = lambda_r - lambda_{r+1},
• dominant rank r from an elbow rule on the singular values.

Reported results from the first tier (CodeAlpaca, two epochs):

• training loss: 2.168
• training accuracy: 60.08 percent
• validation loss: 2.199
• validation accuracy: 60.12 percent
• validation perplexity: 9.02
• holdout perplexity: 9.07
• dominant rank: stable at r = 16
• leading singular values: about [2.383, 2.246]
• Fisher gap Delta_F: about 1.347e-3
• maximum observed |W|: about 0.55

The dominant kernels do not grow only in magnitude; their evolution shows persistent directional structure in phase space. Angular drift statistics stay bounded and accumulate coherently rather than diffusing like an unconstrained random walk. This is treated as an empirical observation, not as evidence of a formal topological invariant.

Context-length diagnostic

A static analysis mode evaluates Fisher gap and phase discretization as a function of input sequence length. This helps detect phase collapse -- the degrading of long-range coherence when distant tokens suffer destructive interference in the complex-valued attention space:

python eval/diag_static.py --context-diagnostic

Output is a JSON record at eval/runs/diag_static_<timestamp>.jsonl containing Fisher gap, kappa, delta (phase discretization), and leading singular values for each tested context length. A phase_collapse_at_ctx field signals whether delta drops below 0.01 at any context length, indicating that phase wrapping or destructive interference may be impeding long-range dependencies.

Topo J-Lens

The Jacobian lens transports intermediate residual-stream activations into the final-layer logit space using the per-layer average input-output Jacobian J_l, letting you inspect what the model "thinks" at each layer before it reaches the output. Based on the jacobian-lens library (Anthropic, Apache 2.0).

Quick demo (CLI)

python -m topogpt3 jlens \
    --checkpoint checkpoints_topogpt3/last \
    --prompts 4 \
    --prompt "def fibonacci(n):\n    "

This loads the checkpoint, fits J_l on 4 auto-generated code prompts, prints Jacobian norm diagnostics, and renders a text_slice table showing the top-1 prediction at select layers for every token position. The table now includes decoded token strings (actual words, not raw token IDs) for every prediction cell.

Python API

from topogpt3.lens_model import TopoGPT3LensModel
from topogpt3.jlens import fit, compute_slice, text_slice

model = TopoGPT3LensModel.from_checkpoint("checkpoints_topogpt3/last")

prompts = [
    "def fibonacci(n):\n    if n <= 1:\n        return n\n",
    "def factorial(n):\n    if n <= 1:\n        return 1\n",
]
lens = fit(model, prompts, source_layers=[0, 1, 2, 3, 4], dim_batch=8)

slice_data = compute_slice(model, lens, "def hello(", top_n=5)
print(text_slice(slice_data))

# slice_data.top_token_strs[pos][layer][k] contains the decoded word
# at layer column `layer`, position `pos`, rank `k` (0-indexed).
print(slice_data.top_token_strs[0][2][0])  # L2 top-1 at position 0

The SliceData object also exposes top_ids, top_probs, token_strs, and layers for custom visualisation.

Run the lens test suite:

pytest tests/test_lens_model.py tests/test_jlens.py -v --tb=short

Pi Agent Harness

The TopoGPT3 API server exposes an OpenAI-compatible HTTP interface so any coding agent that speaks the OpenAI API can drive your local model. This includes Pi, Aider, Continue, and the Codex CLI.

Architecture

+-------------+     Bearer token      +------------------+
| Pi / Aider  | --- HTTP/JSON ------->| TopoGPT3 API     |
| (any agent) | <-- SSE streaming --- | Server :8800     |
+-------------+                       | FastAPI+uvicorn  |
                                      | TopoGPT2 backend |
                                      +------------------+

Security model

The server is hardened against red-team attacks and ships with:

• Authentication: Authorization: Bearer <key> header. Keys are loaded from the TOPOGPT3_API_KEYS env var or --keys. Admin keys (admin: prefix) get higher rate limits. Constant-time comparison prevents timing attacks on the key.

• Rate limiting: token-bucket per IP (10 req/s) and per API key (10 req/s user, 50 req/s admin). Brute-force triggers an IP ban after 10 failed auth attempts within an hour.

• Input hardening: Pydantic strict schemas enforce max prompt length (4096 tokens), max messages (200), max output (4096 tokens), and parameter bounds. Body size is capped at 256 KB.

• Security headers: X-Content-Type-Options, X-Frame-Options, X-XSS-Protection, Content-Security-Policy, and Cache-Control on every response. Server header is suppressed. CORS is deny-all by default with an environment-variable allowlist.

• Audit logging: structured JSON log lines for every request (no secrets ever logged).

Installing the API server

pip install -e ".[api]"

Starting the server

export TOPOGPT3_API_KEYS="sk-my-secret-key-12345,admin:sk-admin-key-67890"

python -m topogpt3 api_server \
    --checkpoint checkpoints_topogpt3/last \
    --port 8800 \
    --host 127.0.0.1

Endpoints:

Method	Path	Auth	Description
POST	/v1/completions	Bearer	Text completions
POST	/v1/chat/completions	Bearer	Chat completions
GET	/v1/models	Bearer	List available models
GET	/health	open	Liveness check

Configuring Pi

Pi discovers models through ~/.pi/agent/models.json. Create it with the TopoGPT3 provider pointing at your local API server:

make pi-setup

This writes a models.json that declares the topogpt3 provider with the OpenAI-compatible completions API. Alternatively, create the file manually:

{
  "providers": {
    "topogpt3": {
      "baseUrl": "http://127.0.0.1:8800/v1",
      "api": "openai-completions",
      "apiKey": "$TOPOGPT3_API_KEY",
      "compat": {
        "supportsDeveloperRole": false,
        "supportsReasoningEffort": false
      },
      "models": [
        {
          "id": "topogpt3",
          "name": "TopoGPT3 (Local)",
          "reasoning": false,
          "input": ["text"],
          "contextWindow": 512,
          "maxTokens": 512,
          "cost": { "input": 0, "output": 0, "cacheRead": 0, "cacheWrite": 0 }
        }
      ]
    }
  }
}

Then run Pi with:

TOPOGPT3_API_KEY="sk-local" make pi-run

The Makefile passes a minimal system prompt to conserve the limited context window.

Configuring other agents

# Aider
export OPENAI_API_BASE="http://localhost:8800/v1"
export OPENAI_API_KEY="sk-my-secret-key-12345"
aider --model openai/topogpt3

# Continue (VS Code extension)
# Set "apiBase" to "http://localhost:8800/v1" in config.json

# Codex CLI
export CODEX_API_BASE="http://localhost:8800/v1"
export CODEX_API_KEY="sk-my-secret-key-12345"

API usage (curl)

# Completions
curl -s http://localhost:8800/v1/completions \
  -H "Authorization: Bearer sk-my-secret-key-12345" \
  -H "Content-Type: application/json" \
  -d '{"prompt":"def fibonacci(n):\n    ","max_tokens":64,"temperature":0.7}'

# Chat completions
curl -s http://localhost:8800/v1/chat/completions \
  -H "Authorization: Bearer sk-my-secret-key-12345" \
  -H "Content-Type: application/json" \
  -d '{
    "messages":[
      {"role":"system","content":"You write Python."},
      {"role":"user","content":"Write a fibonacci function."}
    ],
    "max_tokens":128
  }'

# Streaming chat
curl -s http://localhost:8800/v1/chat/completions \
  -H "Authorization: Bearer sk-my-secret-key-12345" \
  -H "Content-Type: application/json" \
  -d '{"messages":[{"role":"user","content":"fibonacci"}],"stream":true}'

Structured outputs and deterministic seeds

The server adopts the structured-output surface used by DeepInfer and OpenAI. Constrained decoding is powered by xgrammar and works for both streaming and non-streaming requests.

# JSON Schema (OpenAI response_format)
curl -s http://localhost:8800/v1/chat/completions \
  -H "Authorization: Bearer sk-my-secret-key-12345" \
  -H "Content-Type: application/json" \
  -d '{
    "messages":[{"role":"user","content":"Return a person as JSON."}],
    "response_format":{
      "type":"json_schema",
      "json_schema":{
        "name":"person",
        "schema":{
          "type":"object",
          "properties":{"name":{"type":"string"},"age":{"type":"integer"}},
          "required":["name","age"],
          "additionalProperties":false
        }
      }
    },
    "seed":42
  }'

# Regex constraint (DeepInfer-style so_args)
curl -s http://localhost:8800/v1/completions \
  -H "Authorization: Bearer sk-my-secret-key-12345" \
  -H "Content-Type: application/json" \
  -d '{"prompt":"Ticket: ","so_args":{"schema_type":"regex","schema":"[A-Z]{2}-[0-9]{3}"},"seed":7}'

# EBNF grammar
curl -s http://localhost:8800/v1/completions \
  -H "Authorization: Bearer sk-my-secret-key-12345" \
  -H "Content-Type: application/json" \
  -d '{"prompt":"","so_args":{"schema_type":"ebnf","schema":"root ::= \"answer=\" (\"yes\" | \"no\")"}}'

• response_format.type accepts text, json_object, json_schema, regex, and grammar.
• so_args.schema_type accepts json, regex, and ebnf, matching DeepInfer's SamplingParams.
• seed fixes the sampling RNG per request; identical prompts and parameters produce identical output.
• auto_continue cannot be combined with a structured-output constraint.
• xgrammar ships with the api and structured extras: pip install -e ".[api]" or pip install -e ".[structured]".

Inference

Two engines share the same checkpoint:

• topogpt3.inference: standard sampler. Loads weights from safetensors, aligns the architecture configuration against the stored tensors, and performs autoregressive generation with top-k filtering and a repetition penalty.
• topogpt3.inference_hrm: hierarchical recursive reasoning sampler. Adds no new trainable parameters. The pretrained transformer layers are reused as the step function of a low-level and high-level latent refinement loop, with a short persistence window across emitted tokens. Halting is governed by the empirical stabilization of the latent state.

HRM is intended to study iterative latent transport at inference time. At the current training stage it preserves syntactic coherence and formatting but does not yield large qualitative improvements in algorithmic correctness; the diagnostics remain stable while high-level convergence events are rare.

Auto-continuation

When the model hits the end-of-text token (EOS) before producing a structurally complete response, the engine can feed the last incomplete lines back as a continuation prefix and resume generation. This is controlled by the --auto-continue flag, with --max-continuations (default 3) setting the upper bound on continuation rounds.

Detection heuristics check for unclosed code fences (```), unclosed brackets/parentheses, trailing punctuation like , or :, and a minimum character threshold.

python -m topogpt3.inference --auto-continue --max-new 512 --prompt "def main("

Thinking mode (HRM)

--thinking enables a deeper reasoning configuration in the HRM engine. When active, the hierarchical reasoner increases its high-level iterations to 4 and low-level iterations to 8 per emitted token, trading latency for deeper iterative latent refinement. Combines naturally with auto-continuation:

python -m topogpt3.inference_hrm --thinking --auto-continue --max-new 512

C Inference Engine

A standalone C implementation of the TopoGPT3 forward pass, compilable as a static Linux ELF or as a bare-metal MiniOS program. It loads the same safetensors-derived weight files as the Python engine and produces identical output, running 60-70% faster than Python on the same hardware.

Features

• Full transformer forward pass: GQA attention with RoPE, sliding window, RMSNorm, SwiGLU MoE with top-2 routing, quaternion torus spectral layers.
• Hand-rolled math (sqrt, exp, tanh, sin, cos, log) -- no libm dependency in standalone mode.
• In-place radix-2 FFT and 2D FFT for torus spectral layers.
• Weight loading from flat binary files (float32 TG3W or float16 TG16).
• Token decoding via GPT-2 BPE vocabulary file (vocab.bin) or a fallback byte-level encoder.
• Three operating modes: headless (-p), interactive (-i), and file-based (-f).
• Pre-tokenized binary input (-T).
• tok/s timing display via rdtsc.

Building (Linux host)

# Convert weights to flat binary (float32, 94 MB)
python convert_weights.py -i checkpoints_topogpt3/last/model.safetensors \
    -o topogpt3.weights

# Or convert to float16 (47 MB, for MiniOS)
python convert_weights_minios.py -i checkpoints_topogpt3/last/model.safetensors \
    -o topogpt3.fp16

# Generate vocabulary file
# (encode_tokens.py generates tokens.bin for pre-tokenized input;
#  vocab.bin is generated separately and distributed with the project)

# Compile
gcc -static -no-pie -O2 -o topogpt3.elf topogpt3.c -lm

Command-line options

TopoGPT3 - Quaternion Topological Transformer Inference

Usage:
  topogpt3 -h                      Show help
  topogpt3 -p "prompt" [options]   Generate text (headless)
  topogpt3 -i [options]            Interactive mode
  topogpt3 -f file.txt [options]   Read prompt from file
  topogpt3 -T tokens.bin           Read pre-tokenized binary IDs

Options:
  -w FILE    Weight file (default: topogpt3.weights)
  -v FILE    Vocabulary file (default: vocab.bin)
  -n NUM     Max new tokens (default: 256)
  -t NUM     Temperature (default: 0.3)
  -k NUM     Top-k (default: 50)
  -r NUM     Repetition penalty (default: 1.1)

Usage examples

Headless generation:

./topogpt3.elf -w topogpt3.weights -v vocab.bin \
    -p "def fibonacci(n):" -n 100 -t 0.2

Interactive mode:

./topogpt3.elf -w topogpt3.weights -v vocab.bin -i

Inside the interactive session:

TopoGPT3 Inference Engine
Model: small (d=256, heads=8, layers=6, kv=2)
Loading weights from: topogpt3.weights
Loaded vocab: 50257 tokens (321428 bytes)
Loading 380 tensors (v1)...
  Layer 0 loaded
  ...
Weights loaded successfully.
Ready.

interactive mode. /help for commands.
> def fibonacci(n):
    if n <= 1:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

> /temp 0.1
Temperature set to 0.10

> /topk 20
Top-k set to 20

> /status
Model: small (d=256, heads=8, layers=6, kv=2, experts=4, topk=2)
Context: 32 tokens
Parameters: temp=0.10 topk=20 rep=1.10 max=256

> /quit

Interactive mode commands:

/help      Show commands
/quit      Exit
/clear     Clear prompt buffer
/temp N    Set temperature
/topk N    Set top-k
/rep N     Set repetition penalty
/newtokens N  Set max new tokens
/status    Show current settings

Reading a prompt from a file:

./topogpt3.elf -w topogpt3.weights -v vocab.bin -f prompt.txt -n 200

Using pre-tokenized binary input:

# On the host: tokenize text to binary IDs
python encode_tokens.py -t checkpoints_topogpt3/last -p "def hello():" \
    -o tokens.bin

# Run inference from binary token IDs
./topogpt3.elf -w topogpt3.weights -v vocab.bin -T tokens.bin -n 50

Building for MiniOS

TopoGPT3 runs as a ring-3 Linux ELF inside MiniOS, exactly like Lua, MicroPython, and DOOM. The float16 weight format keeps the filesystem footprint under 50 MB.

cd /path/to/miniOS

# Build the binary
make progs/bin/topogpt3.elf

# Rebuild the MiniFS image (includes topogpt3.elf, weights, vocab)
make minifs.bin

# Rebuild the full disk image
make os.img

# Run in QEMU
make run

Inside MiniOS, the program is launched from the shell:

miniOS> topogpt3 -w topogpt3.fp16 -v vocab.bin -p "def fibonacci(n):" -n 30

TopoGPT3 Inference Engine
Model: small (d=256, heads=8, layers=6, kv=2)
Loading weights from: topogpt3.fp16
Loaded vocab: 50257 tokens (321428 bytes)
Loading 380 tensors (fp16 v2)...
  Layer 0 loaded
  ...
Weights loaded successfully (fp16).
Ready.

Prompt: 17 tokens
---

        return n

---
Generated 12 tokens in 15.03s (0.80 tok/s)

The 0.80 tok/s figure reflects QEMU software emulation without KVM. With KVM acceleration, performance matches the Linux host (17-29 tok/s).

Interactive mode also works inside MiniOS:

miniOS> topogpt3 -w topogpt3.fp16 -v vocab.bin -i

interactive mode. /help for commands.
> def bubble_sort(arr):
    n = len(arr)
    for i in range(n):
        for j in range(0, n-i-1):
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j]
    return arr

> /quit

Weight formats

The engine supports two binary weight formats, auto-detected at load time:

Format	Magic	Precision	Size	Description
TG3W	TG3W	float32	94 MB	Full-precision, Linux host use
TG16	TG16	float16	47 MB	Half-precision, MiniOS use

Convert between formats:

# float32 (default)
python convert_weights.py -i checkpoints_topogpt3/last/model.safetensors \
    -o topogpt3.weights

# float16 (for MiniOS)
python convert_weights_minios.py -i checkpoints_topogpt3/last/model.safetensors \
    -o topogpt3.fp16

Files on MiniFS

When built for MiniOS, the following files are placed on the MiniFS filesystem:

File	Size	Description
topogpt3.elf	841 KB	Static Linux ELF binary
topogpt3.fp16	47 MB	Float16 model weights
vocab.bin	422 KB	GPT-2 BPE vocabulary (50257 tokens)

Performance

Measured on the prompt def fibonacci(n): with 30 new tokens, temperature 0.3, top-k 50:

Platform	Speed
Linux host (KVM)	17-29 tok/s
Linux host (no KVM)	8-12 tok/s
MiniOS QEMU (no KVM)	0.80 tok/s
MiniOS QEMU (with KVM)	17-29 tok/s

<<<<<<< HEAD

Advanced training (beyond the curriculum)

Everything below extends the base curriculum trainer while keeping the quaternionic / spectral / topological identity intact: adapters are additive, losses wrap the existing TopoGPT2.forward, and no architecture is rewritten. Details per feature with a runnable example. Short form via Makefile (make help lists all targets); long form is python -m topogpt3 <subcommand> — the topogpt3-* console scripts from pip install -e . also work (e.g. topogpt3-train-lora). Bare train-lora does not exist as a shell command; use make train-lora or python -m topogpt3 train-lora.

0. Export the real curriculum to chat JSONL (topogpt3/export_chat.py — same HF datasets + fallbacks as train.py, tiers 0–2 as conversations):

python -m topogpt3 export-chat --out-dir data/chat --max-per-tier 20000
# -> data/chat/{sft_all,rlaif_all,dpo_all}.jsonl + pretrain_tier3.jsonl

1. YaRN long-context RoPE (topogpt3/yarn.py — only rescales rotary frequencies, never the spectral layers):

from topogpt3.model import TopoGPT2, TopoGPT2Config
m = TopoGPT2(TopoGPT2Config(SCALE="small"))
m.layers[0].attn.rope.enable_yarn(factor=16.0, orig_max=2048)

2. Chat template + tool/thinking tokens (topogpt3/chat.py — string level, no BPE retraining; <tool_call>, <think>, <|bufferN|>):

from topogpt3.chat import apply_chat_template, split_reasoning_content
p = apply_chat_template([{"role": "user", "content": "Compute 12*34"}],
                        tools=[{"type": "function", "function": {"name": "calculate_math"}}],
                        add_generation_prompt=True, open_thinking=True)
split_reasoning_content("<think>plan</think> 408")  # -> reasoning_content/content/tool_calls

3. Native LoRA SFT (topogpt3/lora.py + train_lora.py — patches Q/K/V/O plus quaternion sub-linears Ww/Wx/Wy/Wz, zero-init, base frozen):

python -m topogpt3 train-lora --data data/chat/sft_all.jsonl \
    --checkpoint checkpoints_topogpt3/last --out out/topo_lora.pt --rank 16

4. DPO preference alignment (topogpt3/train_dpo.py — policy + frozen ref, beta=0.15, MoE aux preserved):

python -m topogpt3 train-dpo --data data/chat/dpo_all.jsonl \
    --checkpoint checkpoints_topogpt3/last --out out/topo_dpo.pt

5. GRPO / CISPO RLAIF (topogpt3/train_grpo.py — group advantages, k3 KL to ref, no Critic, ideal for complex weights):

python -m topogpt3 train-grpo --data data/chat/rlaif_all.jsonl --loss-type cispo \
    --num-generations 4 --checkpoint checkpoints_topogpt3/last --out out/topo_grpo.pt

6. PPO + value head (topogpt3/train_ppo.py — critic reuses the frozen trunk + fresh Linear head):

python -m topogpt3 train-ppo --data data/chat/rlaif_all.jsonl \
    --checkpoint checkpoints_topogpt3/last --out out/topo_ppo.pt

7. White-box distillation (topogpt3/train_distill.py — CE + T²·KL on response tokens, teacher HF or self-copy):

python -m topogpt3 train-distill --data data/chat/sft_all.jsonl \
    --teacher Qwen/Qwen2.5-Coder-0.5B --checkpoint checkpoints_topogpt3/last \
    --out out/topo_distill.pt

8. Agentic RL with tools (topogpt3/train_agent.py + tools_agent.py — multi-turn <tool_call> → sandboxed run_python → <tool_response>):

python -m topogpt3 train-agent --data data/agent_rl.jsonl --max-turns 3 \
    --checkpoint checkpoints_topogpt3/last --out out/topo_agent.pt

9. Merge LoRA / export (topogpt3/convert.py — weights preserved verbatim):

python -m topogpt3 convert --base checkpoints_topogpt3/last \
    --lora out/topo_lora.pt --out out/topo_merged --hf-stub

10. API with reasoning + tools (api_server.py — splits <think> / <tool_call> into reasoning_content / tool_calls, accepts tools + open_thinking):

curl -s http://localhost:8800/v1/chat/completions \
  -H "Authorization: Bearer $TOPOGPT3_API_KEYS" -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "Compute 12*34"}],
       "tools": [{"type": "function", "function": {"name": "calculate_math"}}],
       "open_thinking": true}'

11. Tool-call eval (topogpt3/eval_toolcall.py):

from topogpt3.eval_toolcall import evaluate
evaluate(lambda prompt: '<tool_call>{"name": "calculate_math", '
                        '"arguments": {"expression": "1+1"}}</tool_call>')

12. DDP + resume (topogpt3/trainer_utils_topo.py — cosine LR, SkipBatchSampler, atomic double-save with cross-GPU step rescale):

torchrun --nproc_per_node 2 -m topogpt3 train-lora --data data/chat/sft_all.jsonl

Shared math lives in topogpt3/rewards.py (grpo_loss, dpo_loss_fn, distillation_loss, spectral_bonus) and rollouts in topogpt3/rollout.py (TorchRolloutEngine over the model's own generate()). Heritage tests: pytest tests/test_heritage.py.

75d7832 (Agrega código de LoRA e ignora datasets/modelos pesados)

Repository layout

.
├── topogpt3/                  pip-installable package
│   ├── __init__.py            public API re-exports
│   ├── model.py               base TopoGPT2 architecture, tokenizer, helpers
│   ├── train.py               curriculum trainer + Grassmannian diagnostics
│   ├── inference.py           standard autoregressive sampler
│   ├── inference_hrm.py       hierarchical recursive reasoning sampler
│   ├── continuation.py        auto-continuation engine (detect + resume truncated output)
│   ├── lens_model.py          Jacobian-lens model adapter (LensModel protocol)
│   ├── jlens.py               Jacobian lens fitting + application pipeline
│   └── api_server.py          OpenAI-compatible HTTP API server (hardened)
│   ├── yarn.py                YaRN RoPE extrapolation for long context
│   ├── chat.py                chat template + <tool_call>/<think>/<|buffer|> tokens
│   ├── datasets_chat.py       SFT/DPO/RLAIF/Agent JSONL datasets
│   ├── lora.py                native quaternion-safe LoRA (apply/save/merge)
│   ├── rewards.py             GRPO/DPO/distillation losses + spectral bonus
│   ├── rollout.py             TorchRolloutEngine over TopoGPT2.generate
│   ├── trainer_utils_topo.py  cosine LR, DDP, SkipBatchSampler, atomic checkpoints
│   ├── train_lora.py          SFT with LoRA adapters
│   ├── train_dpo.py           preference alignment (DPO + frozen ref)
│   ├── train_grpo.py          RLAIF (GRPO/CISPO, no critic)
│   ├── train_ppo.py           RLAIF (PPO + trunk-reusing critic)
│   ├── train_distill.py       white-box distillation (CE + T²·KL)
│   ├── train_agent.py         agentic RL (multi-turn tool use)
│   ├── tools_agent.py         run_python sandbox + mock tools + rollout helper
│   ├── convert.py             merge LoRA / HF stub export
│   ├── export_chat.py         curriculum HF -> chat JSONL exporter
│   └── eval_toolcall.py       tool-call accuracy evaluator
├── tests/                     BDD test suite
│   ├── test_lens_model.py     adapter contract tests
│   └── test_jlens.py          fitting + application contract tests
├── eval/                      HumanEval benchmark harness
│   ├── harness.py             evaluation pipeline
│   ├── samplers.py            sampler registry (standard / HRM)
│   ├── sandbox.py             sandboxed test executor
│   ├── analysis.py            pass@k / metrics reporting
│   └── diag_static.py         static checkpoint diagnostics (kappa, delta, winding, context-length)
├── topogpt3.c                 standalone C inference engine (~2000 lines)
├── convert_weights.py         safetensors to float32 binary converter
├── convert_weights_minios.py  safetensors to float16 binary converter (MiniOS)
├── encode_tokens.py           GPT-2 BPE tokenizer to binary IDs
├── vocab.bin                  GPT-2 BPE vocabulary (50257 tokens, 422 KB)
├── topogpt3.weights           float32 weights (94 MB, generated)
├── topogpt3.fp16              float16 weights (47 MB, generated)
├── topogpt3.elf               static Linux ELF binary (generated)
├── app.py                     example entry point for downstream projects
├── Makefile                   convenience targets for all common commands
├── pyproject.toml             package metadata, dependencies, console scripts
├── README.md                  this file
├── topogpt3.md                full paper write-up
├── quickstart.md              five-minute getting started guide
├── tutorial.md                step-by-step usage tutorial
├── essentials.md              core concepts explained
├── cheatsheet.md              command and API quick reference
├── comparison.md              comparison with similar models
├── claude.md                  integration guide for Claude and Anthropic
├── synthetic_dataset.py       optional synthetic dataset helper
├── docs/                      HTML documentation and assets
└── workflows/                 GitHub Actions workflows

Requirements

• Python 3.10 or newer
• PyTorch with CUDA recommended (CPU works for small scales)
• safetensors
• tiktoken (BPE tokenizer)
• numpy
• datasets and huggingface-hub for data preparation (optional extra [train])

Installation

From a checkout of this repository:

pip install -e .

Extra dependencies:

pip install -e ".[train]"   # datasets, huggingface-hub
pip install -e ".[lens]"    # huggingface-hub
pip install -e ".[api]"     # fastapi, uvicorn (for the agent harness)
pip install -e ".[dev]"     # pytest, ruff

Or install everything at once:

pip install -e ".[train,lens,api,dev]"

Once published, the package will be installable directly from PyPI or GitHub:

pip install topogpt3
pip install git+https://github.com/grisuno/TopoGPT3

The install registers these console scripts:

Command	Description
topogpt3-train	Full curriculum trainer CLI
topogpt3-infer	Standard autoregressive sampler CLI
topogpt3-infer-hrm	Hierarchical recursive reasoning sampler CLI
topogpt3-jlens	Jacobian lens demo (fit + slice + text table)
topogpt3-api	OpenAI-compatible API server (agent harness)

Using the package from your own code

The simplest pattern is to import the public API from topogpt3 and build the settings object that matches the use case:

from topogpt3 import InferenceSettings, InferencePipeline

settings = InferenceSettings(
    checkpoint_dir="checkpoints_topogpt3",
    checkpoint_name="last",
    prompt="def fibonacci(n):\n",
    max_new_tokens=200,
)
report = InferencePipeline(settings).execute()
print(report.output)

For hierarchical recursive inference:

from topogpt3 import (
    HRMInferencePipeline,
    HRMInferenceSettings,
    RecursiveReasoningConfig,
)

settings = HRMInferenceSettings(
    prompt="def fibonacci(n):\n",
    reasoning=RecursiveReasoningConfig(
        max_high_level_iters=2,
        max_low_level_iters=3,
        low_level_window=2,
    ),
)
report = HRMInferencePipeline(settings).execute()
print(report.output)

Jacobian Lens

Read out intermediate residual activations in the final-layer basis using the average input-output Jacobian. Based on the jacobian-lens library (Anthropic, Apache 2.0).

from topogpt3.lens_model import TopoGPT3LensModel
from topogpt3.jlens import fit, JacobianLens

model = TopoGPT3LensModel.from_checkpoint("checkpoints_topogpt3/last")

prompts = [
    "def fibonacci(n):\n    if n <= 1:\n        return n\n",
    "def factorial(n):\n    if n <= 1:\n        return 1\n",
]

lens = fit(model, prompts, source_layers=[0, 1, 2, 3, 4], dim_batch=8, max_seq_len=128)

lens_logits, model_logits, input_ids = lens.apply(
    model, "def hello(", layers=[0, 2, 4], use_jacobian=True
)

Run the test suite (no GPU required):

pytest tests/test_lens_model.py tests/test_jlens.py -v --tb=short

app.py at the repository root is a complete, runnable example that wires both inference modes plus the trainer behind a tiny --mode CLI. It is intended to be copied into downstream projects and adapted.

Command-line usage

After pip install -e .:

Prepare datasets (downloads and tokenizes the four tiers into local cache):

topogpt3-train --prepare-data

Run the full curriculum:

topogpt3-train --train

Start from a specific tier and re-train from there (the --start-tier flag is honored even if the tier is already marked completed in the checkpoint state):

topogpt3-train --train --start-tier 2

Evaluate on the combined holdout:

topogpt3-train --eval-holdout

Standard inference from the latest checkpoint:

topogpt3-infer --prompt "def fibonacci(" --max-new 200

Inference with auto-continuation:

topogpt3-infer --auto-continue --max-new 512 --prompt "def main("

Hierarchical recursive inference:

topogpt3-infer-hrm --prompt "def fibonacci(" \
    --hrm-h-iters 2 --hrm-l-iters 3 --hrm-l-window 2 --max-new 200

HRM thinking mode with auto-continuation:

topogpt3-infer-hrm --thinking --auto-continue --max-new 512 --prompt "def main("

Jacobian lens visualization:

topogpt3-jlens --checkpoint checkpoints_topogpt3/last \
    --prompts 4 --prompt "def fibonacci(n):\n    "

Start the API server for Pi / Aider / Continue:

export TOPOGPT3_API_KEYS="sk-my-secret-key-12345"
topogpt3-api --checkpoint checkpoints_topogpt3/last --port 8800

The same entry points are reachable as modules (useful before installation):

python -m topogpt3.train --help
python -m topogpt3.inference --help
python -m topogpt3.inference_hrm --help
python -m topogpt3.jlens --help
python -m topogpt3 api_server --help
python app.py --mode infer --prompt "def main(" --max-new 64

Or through the unified dispatcher:

python -m topogpt3 infer --auto-continue --max-new 512
python -m topogpt3 infer-hrm --thinking --auto-continue
python -m topogpt3 train
python -m topogpt3 api
python -m topogpt3 export-chat --out-dir data/chat --max-per-tier 20000
python -m topogpt3 train-lora --data data/chat/sft_all.jsonl
python -m topogpt3 train-dpo --data data/chat/dpo_all.jsonl
python -m topogpt3 train-grpo --data data/chat/rlaif_all.jsonl --loss-type cispo
python -m topogpt3 train-ppo --data data/chat/rlaif_all.jsonl
python -m topogpt3 train-distill --data data/chat/sft_all.jsonl
python -m topogpt3 train-agent --data data/agent_rl.jsonl
python -m topogpt3 convert --base checkpoints_topogpt3/last --lora out/topo_lora.pt --out out/topo_merged

Makefile

A Makefile at the repo root wraps every common task. Run make help to see all targets.

make install           pip install -e ".[train,lens,api,dev]"
make test              run full test suite
make lint              ruff check + format
make train             full curriculum training
make infer             standard inference (prompt=def fibonacci)
make infer-continue    inference with auto-continuation
make infer-hrm         HRM inference
make infer-think       HRM thinking mode with auto-continuation
make jlens             Jacobian lens demo (fit 4 prompts)
make api               start API server on port 8800 (no auth)
make api-auth          start API server with authentication
make eval              HumanEval benchmark (all 164 problems)
make eval-sample       HumanEval single problem
make pi                clone, build, and configure Pi agent
make pi-setup          write TopoGPT3 provider config to ~/.pi/agent/models.json
make pi-run            launch Pi pointed at local TopoGPT3 API
make c-convert         convert weights to float32 binary
make c-vocab           generate vocab.bin
make c-tokenize        tokenize text to binary IDs
make c-build           compile C inference engine
make c-run             run C engine (headless, prompt=def fibonacci)
make c-run-i           run C engine (interactive mode)
make c-all             convert + build + run
make clean             remove __pycache__ and .pyc files

Checkpoint compatibility

The model is always built with the maximum sequence length across all curriculum tiers, so positional embeddings keep a fixed shape regardless of which tier is used as the entry point. Existing safetensors weights load without shape mismatch when restarting at a different tier.

As of the 2026-08 sliding window update, the RoPE (Rotary Position Embedding) caches are stored as non-persistent buffers under the names _cos_cache and _sin_cache. Checkpoints from earlier versions that contain cos_cache and sin_cache are safely ignored during loading with strict=False. The caches are recomputed to match the current MAX_SEQ_LEN configuration at model instantiation time. Memory tokens (memory_tokens) are a new parameter introduced alongside the latent compression feature; missing this key in older checkpoints is harmless and produces a warning during load.

Limitations

This is an exploratory small-scale study. The model is only 24.5M parameters and is trained on a limited curriculum. The phase and angular drift measurements are diagnostics, not rigorous mathematical invariants. A real-valued control of the same parameter count, broader benchmarks, and longer training are needed before drawing stronger conclusions.

Early generations show syntactic continuity and local semantic consistency. Algorithmic correctness remains limited at this scale and training duration.

Related work

A 25M-parameter Transformer implementation designed to study language acquisition as a condensed matter phenomenon. Unlike traditional LLMs, TopoGPT-2 is engineered to reach a Topological Insulator state a phase where grammatical and logical invariants are protected by a spectral gap. Using the Tiny Stories corpus

• https://github.com/grisuno/TopoGPT2

Citation

If you build on this work, please cite:

• https://doi.org/10.5281/zenodo.20388757

grisuno, "TopoGPT3: Exploring Complex-Valued Representations in Small
Code Models", May 2026.

License

AGPL v3.

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Topo Journey

If you are interested in this project, explore the architecture evolution:

• TopoBrain: Topo applied to neural networks.
• ReadMenator: Knowledge graph generator without LLMs.
• TopoGPT2: Previous evolution of the model.

Knowledge Base

This project has been analyzed by ReadMenator, a zero-token polyglot static analysis tool. Regenerate with pip install readmenator && readmenator . --rebuild. Analysis outputs:

• KNOWLEDGE_BASE.md -- Full architecture reference with all classes, functions, imports, dependency graphs, UML class diagrams, security audit findings, community analysis, taint paths, hotspots, cycles, and more.
• readmenator-agent/ -- Agent-friendly, grep-optimized index.
  • INDEX.md -- Quick reference: what each file does
  • SYMBOLS.md -- Every symbol with file:line
  • ARCHITECTURE.md -- Internal dependency pairs + external imports
  • API.md -- Public function contracts
  • GOTCHAS.md -- Change warnings (blast radius, cycles, hotspots)
  • SECURITY.md -- Findings by severity with fix hints
  • MEMORY.md -- Rules, workflow, style, deliverables, session log
  • MANIFEST.json -- Freshness (git_commit + source_fingerprint) + read order
  • KB_<subsystem>.md -- Per-subsystem context
  • recipes/*.md -- Grounded task guides (add-function, fix-cycle, ...)
• readmenator-wiki/ -- Navigable wiki (start here for the big picture).
  • index.md -- Entry point: overview, reading order, god nodes, connections
  • community_*.md -- One synthesis page per code community
  • connections.json -- Typed bridges between communities
  • queries.md -- Suggested questions + answer log
  • REPORT.md -- Honest audit: coverage, confidence, limits
• readmenator-graphrag/ -- Zero-token GraphRAG index.
  • index.json + REPORTS.md -- Query with readmenator . ask "<question>"
• readmenator-apidocs/ -- API reference (Sphinx/Doxygen style).
  • index.md + modules/*.md -- Signatures, parameters, returns, raises, examples, call graphs
  • architecture.md -- Tiered architecture + execution flows (control flow, call tree, sequence)
  • classes.md, genindex.md, COVERAGE.md -- Hierarchy, A-Z index, doc coverage + warnings
  • index.html + api/ -- Offline searchable browser and machine-readable model (api/index.json, api/modules/*.jsonl one entity per line)
• readmenator-maps/ -- Interactive gallery (index.html).
  • 5 system maps: architecture, workflow, sequence, dataflow, lifecycle
  • graph-force.html -- Force-graph explorer (2D/3D, reach, paths, lenses)
  • graph-bundle.html -- Edge bundle explorer (circle + sphere)
  • components.html -- Component diagrams
• docs/ -- Static site (GitHub Pages ready).
  • Gallery copy + llms.txt (agent entry point) + md/ docs + video
• readmenator-diagrams/ -- Component diagrams.
  • analysis.json + graph LR markdown + per-component pages + index.html
• readmenator-rules/ -- Suggested Semgrep rules (*.yml), yara_rules.yar (YARA-lite) + exclusions.yaml (false-positive blocklist)
• readmenator.mp4 -- Cinematic synthwave overview video
• .cursorrules -- Generated AI assistant rules
• readmenator_audit.sarif -- SARIF v2.1.0 audit (when security scan is on)

Cache (.readmenator_cache/), packaged agent skills (.claude/skills/), and the GitHub wiki mirror are also produced on run/--rebuild.

AI agents: Read readmenator-wiki/index.md first for the big picture, then readmenator-agent/INDEX.md for grep-friendly lookup. Developers: Read KNOWLEDGE_BASE.md for full architecture reference, open readmenator-maps/index.html for the visual gallery.

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