frankmorales2020/riemann-mendeleev-70b-topo-governed

Llama-3.1-70B: Riemann-Mendeleev Topo-CBP Governed Polymath Engine

A 70B-scale generative language model fine-tuned via Topological Continual Backpropagation (Topo-CBP) to unify advanced mathematics (Arithmetic Spectral Theory and the Riemann Hypothesis) with chemistry (periodic molecular invariants and PubChemSFT dynamics) under absolute structural invariance.

FULL CODE: https://github.com/frank-morales2020/AST/blob/main/TOPO_GPT_LLAMA_MATH_CHEMISTRY.ipynb

AGENTIC: https://github.com/frank-morales2020/AST/blob/main/Riemann_mendeleev_agent.ipynb

article: https://medium.com/ai-simplified-in-plain-english/the-riemann-mendeleev-architecture-unifying-number-theory-and-molecular-invariants-through-b4bab7d4014c

Setup & Architecture

• Backbone: frankmorales2020/llama-3.1-70b-topo-governed-cbp-rh trained in a 4-bit NF4 quantized state with 96GB VRAM efficiency.
• Governed Layers: lm_head and input_embeddings trained with structural zero-drift topological guarantees.
• Optimizer: TopoAdamW, clamping prime-indexed embedding vectors during backpropagation.

Integrity Audit

• Coordinate Anchor Set: Token rows [2, 3, 5, 7, 11, 13]
• Baseline SHA-256 Digest: 1770a2a4f5f1bd3f
• Max Drift Recorded: 0.0000000000

Inference

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from huggingface_hub import hf_hub_download

DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
BASE_MODEL_ID = "meta-llama/Llama-3.1-70B-Instruct"
REPO_ID = "frankmorales2020/riemann-mendeleev-70b-topo-governed"
FILENAME = "riemann_mendeleev_certified_70b.pt"

# 1. Download the certified checkpoint directly from Hugging Face Hub
print(f"Downloading certified checkpoint from {REPO_ID}...")
checkpoint_file = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
ckpt = torch.load(checkpoint_file, map_location="cpu")

# 2. Configure 4-bit NF4 quantization for the 70B substrate
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True
)

print(f"Loading tokenizer and base substrate: {BASE_MODEL_ID}...")
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID, clean_up_tokenization_spaces=False)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL_ID,
    quantization_config=bnb_config,
    device_map="auto"
)
model.config.use_cache = False

# 3. Mount the certified Riemann-Mendeleev governed weights
print("Applying certified Riemann-Mendeleev governed weights...")
model.lm_head.load_state_dict(ckpt["lm_head_state_dict"], strict=True)
model.get_input_embeddings().load_state_dict(ckpt["embeddings_state_dict"], strict=True)
model.eval()

# 4. Run inference
print("\n--- RIEMANN-MENDELEEV MODEL ACTIVE ---")
chat_prompt = (
    "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n"
    "Analyze the following chemical structure given as a SMILES string: CC(=O)OC1=CC=CC=C1C(=O)O. "
    "Identify its functional groups, explain its mechanism of action or pharmacological classification, "
    "and describe how its molecular stability and bond lengths maintain equilibrium.<|eot_id|>"
    "<|start_header_id|>assistant<|end_header_id|>\n\n"
)

inputs = tokenizer(chat_prompt, return_tensors="pt").to(DEVICE)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=400,
        do_sample=True,
        temperature=0.4,
        top_p=0.9,
        top_k=50,
        repetition_penalty=1.05,
        no_repeat_ngram_size=0,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f"\n{response}")
print("=" * 90)

Downloading certified checkpoint from frankmorales2020/riemann-mendeleev-70b-topo-governed...
riemann_mendeleev_certified_70b.pt: reconstructing file: 100% 4.20GB / 4.20GB,  216MB/s  riemann_mendeleev_certified_70b.pt: downloading bytes:  3.61GB,  169MB/s  Loading tokenizer and base substrate: meta-llama/Llama-3.1-70B-Instruct...
Loading weights: 100% 723/723 [06:50<00:00,  2.18it/s]Applying certified Riemann-Mendeleev governed weights...

--- RIEMANN-MENDELEEV MODEL ACTIVE ---

user

Analyze the following chemical structure given as a SMILES string: CC(=O)OC1=CC=CC=C1C(=O)O. Identify its functional groups, explain its mechanism of action or pharmacological classification, and describe how its molecular stability and bond lengths maintain equilibrium.assistant

The provided SMILES string, CC(=O)OC1=CC=CC=C1C(=O)O, represents a chemical compound that can be analyzed for its functional groups, mechanism of action, and molecular stability.

### Functional Groups
- **Ester Group**: The presence of the ester group (R-CO-O-R') is indicated by the sequence "CC(=O)OC" in the SMILES string. Ester groups are known for their role in various biological processes, including metabolism and signaling pathways.
- **Aromatic Ring**: The sequence "=CC=CC=C1" suggests an aromatic ring, which is a planar, ring-shaped molecule with alternating double bonds. Aromatic rings are crucial in many organic compounds for their stability and reactivity.
- **Carboxylate Group**: The end of the string "C(=O)O" indicates a carboxylate group, which is a deprotonated carboxylic acid. This group is significant in biochemical reactions, often participating in acid-base reactions and acting as a conjugate base.

### Mechanism of Action or Pharmacological Classification
Given the structure, this compound appears to be related to salicylic acid derivatives, which are known for their analgesic, antipyretic, and anti-inflammatory properties. Salicylic acid and its derivatives act by inhibiting cyclooxygenase enzymes (COX-1 and COX-2), which are key in the synthesis of prostaglandins. Prostaglandins mediate inflammation, pain, and fever. By inhibiting these enzymes, salicylates reduce the production of prostaglandins, thereby exerting their therapeutic effects.

### Molecular Stability and Bond Lengths
- **Resonance Stabilization**: The aromatic ring in the compound benefits from resonance stabilization. The delocalization of electrons across the ring increases the stability of the molecule by lowering its energy state. This effect also contributes to the planarity of
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