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

deepseek-governed-no-amnesia

An experimental repository pairing DeepSeek-R1-Distill-Qwen-7B with a "Prime-Anchored Spectral Governor" artifact. This card describes exactly what the repository contains and what the accompanying run did and did not do.

What this repository is

The model weights are identical to the base model, deepseek-ai/DeepSeek-R1-Distill-Qwen-7B. The governor did not modify them.
Attached is governor_state.pt, which records the governor configuration (anchor primes, threshold, cached anchor rows, gate statistics, and a SHA-256 signature of the prime-indexed embedding rows).
VERIFICATION.txt records the run details.

What the governor does (and doesn't)

The governor evaluates a gate on each training step and pins six prime-indexed embedding rows ([2, 3, 5, 7, 11, 13]) to their original values. In the run that produced this repository, the optimizer step was a no-op (STEP_GATED_NO_MUTATION), so the model was not trained or fine-tuned.

As a result:

The prime-indexed rows are unchanged (anchor signature matches).
The full model is byte-for-byte the base model.
This run does not demonstrate training, fine-tuning, or that any form of catastrophic forgetting was prevented, because the model was not modified. The repository name reflects the project's intent, not a measured property of this checkpoint.

Intended use

Research and experimentation with the governor tooling. For general use, loading the base model directly is equivalent and avoids downloading a duplicate copy of the weights.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

REPO_ID = "frankmorales2020/deepseek-governed-no-amnesia"

tokenizer = AutoTokenizer.from_pretrained(REPO_ID, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    REPO_ID, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True
)
model.eval()

prompt = "Explain why prime numbers are important in cryptography."
text = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    tokenize=False, add_generation_prompt=True,
)
enc = tokenizer(text, return_tensors="pt").to(model.device)

out = model.generate(
    **enc,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    repetition_penalty=1.3,
    no_repeat_ngram_size=3,
    pad_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True))

This is a DeepSeek R1 distill (reasoning) model. Feed prompts through the chat template, and expect a <think> ... </think> reasoning trace before the final answer. To keep only the final answer, split the output on </think>.

Inspecting the governor artifact

from huggingface_hub import hf_hub_download
import torch

state = torch.load(
    hf_hub_download(REPO_ID, "governor_state.pt"),
    map_location="cpu", weights_only=False,
)
print(state["primes"], state["LAMBDA_12"], state["anchor_signature"], state["stats"])

License

Released under the MIT license, inherited from the base model.

Acknowledgements

Base model: DeepSeek-R1-Distill-Qwen-7B by DeepSeek-AI.

frankmorales2020/deepseek-governed-no-amnesia