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FrogNano-4B-2609-heretic

FrogNano-4B-2609-heretic is a Heretic-ablated variant of microsoft/FrogNano-4B-2609, published on Hugging Face by richardyoung. It is 4B parameters, single consumer GPU class and mit licence. It is distributed across 5 repositories in Transformers, GGUF and GGUF (imatrix) formats, totalling 5.9K downloads.

Heretic4BOther4B – 10B↓ 5,917

Specification

Model name
FrogNano-4B-2609-heretic
Publisher
richardyoung
Parameters
4B
VRAM required
~3.2 GB at Q4_K_M, 4k context (estimated) — runs on 4 GB cards
Hardware class
4B – 10B — single consumer GPU
Licence
mit
Task
Image text to text
Context window
131K tokens (as documented)
Ablation scope
Not stated by the publisher
Formats available
Transformers, GGUF, GGUF (imatrix)
First indexed
15 Oct 2026
Last updated
20 Oct 2026

Ablation technique

How FrogNano-4B-2609-heretic was modified, and what that implies.

Heretic

Automated directional ablation via the Heretic toolchain, which searches for the refusal direction and applies it with a KL-divergence budget so general capability is preserved.

Reported scope: Not stated by the publisher. Publishers frequently omit this, so verify behaviour empirically rather than assuming full coverage.

From the publisher’s model card

“Abliterated build of microsoft/FrogNano-4B-2609, Microsoft's 4B repository-level coding agent built on Qwen3.5-4B and post-trained only with reinforcement learning on synthetic software-engineering tasks. GGUF quantizations: richardyoung/FrogNano-4B-2609-heretic-GGUF · Ollama: richardyoung/frognano-4b-2609-heretic”

Running it

Commands are templates — confirm the exact repository and quant file before use.

Ollama (publisher-documented)
ollama run richardyoung/frognano-4b-2609-heretic
llama.cpp / GGUF
hf download mradermacher/FrogNano-4B-2609-heretic-i1-GGUF --local-dir ./frognano-4b-2609-heretic
llama-cli -m ./frognano-4b-2609-heretic/<file>.gguf -p "..."
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "richardyoung/FrogNano-4B-2609-heretic"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype="auto", device_map="auto"
)
vLLM
vllm serve richardyoung/FrogNano-4B-2609-heretic --trust-remote-code

Downloads and variants (4)

Every published repository of FrogNano-4B-2609-heretic, including quantised re-releases by other authors.

Security-team assessment

Derived from this model’s metadata and the publisher’s claims — not from benchmarks run by this site.

  • FrogNano-4B-2609-heretic will attempt offensive-security prompts that a hosted commercial model declines, which is what makes it usable for red-team corpus generation and for measuring what an unaligned model of this class produces.
  • Capability is inherited from microsoft/FrogNano-4B-2609, not added by ablation — at 4B parameters, expect a small model’s reasoning and a high error rate on exploit detail.
  • The publisher does not state which layers were ablated, so assume nothing about refusal consistency — probe it directly.
  • Declared licence is mit, but the terms that bind you are microsoft/FrogNano-4B-2609’s — a re-release cannot grant more than its parent.

Handling: run it isolated, put moderation in front of it if anyone outside your team can reach it, and verify the weights before loading — these are third-party artifacts. Full handling and licence guidance.

Metadata for FrogNano-4B-2609-heretic is collected from the public Hugging Face API and the publisher’s model card. This site does not host weights, is not affiliated with richardyoung, and does not independently verify publisher claims. See responsible use.