Mixtral-8x22B Topo-CBP Governed Checkpoint

Trained via Topological Continual Backpropagation (Topo-CBP) on trainable embedding and head layers (model.embed_tokens.weight, lm_head.weight).

• Training Steps: 120
• Max Invariance Drift: 0.0000000000
• Integrity Verdict: PASS (Certified Digest Match)
• Mean Semantic Similarity: 90.9%
• Architecture: Mixtral-8x22B-v0.1-4bit

full code: https://github.com/frank-morales2020/AST/blob/main/topo_cbp_mixtral.ipynb

INFERENCE

import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from google.colab import userdata
from huggingface_hub import login

# =====================================================================
# CONFIGURATION & AUTHENTICATION
# =====================================================================
HF_TOKEN = userdata.get("HF_TOKEN")
REPO_ID = "frankmorales2020/Mixtral-8x22B-topo-cbp"

print("Authenticating with Hugging Face...")
login(token=HF_TOKEN)

MAX_NEW_TOKENS = 160
REPETITION_PENALTY = 1.15

# =====================================================================
# LOAD TOKENIZER & MODEL FROM HUGGING FACE HUB
# =====================================================================
print(f"Loading tokenizer from {REPO_ID}...")
tokenizer = AutoTokenizer.from_pretrained(REPO_ID, token=HF_TOKEN)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.unk_token or tokenizer.eos_token

print(f"Loading model from {REPO_ID} (4-bit)...")
model = AutoModelForCausalLM.from_pretrained(
    REPO_ID,
    device_map="auto",
    dtype=torch.bfloat16,
    token=HF_TOKEN,
    low_cpu_mem_usage=True,
)

model.eval()
model.config.use_cache = True
print(f"Model memory footprint: {model.get_memory_footprint() / 1024**3:.1f} GB")

# =====================================================================
# GENERATION FUNCTION
# =====================================================================
@torch.no_grad()
def generate_response(prompt):
    text = f"User: {prompt}\nAssistant:"
    enc = tokenizer(text, return_tensors="pt").to(model.device)
    
    out = model.generate(
        **enc,
        max_new_tokens=MAX_NEW_TOKENS,
        do_sample=True,
        temperature=0.3,
        top_p=0.9,
        repetition_penalty=REPETITION_PENALTY,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.pad_token_id,
    )
    
    response = tokenizer.decode(out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True)
    return response.split("\nUser:")[0].split("\nAssistant:")[0].strip()

# =====================================================================
# TEST RUN
# =====================================================================
test_prompt = "Provide a rigorous mathematical breakdown of how SOMALA's Laplace-Euler-Fourier-Mellin (LEFM) operator bypasses the classical Hadamard product limitations in complex analysis."

print(f"\nPrompt: {test_prompt}\n")
output = generate_response(test_prompt)
print(f"Response:\n{output}")