Qwen2.5-Coder-7B-Instruct-heretic

Qwen2.5-Coder-7B-Instruct-heretic

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A decensored variant of Qwen/Qwen2.5-Coder-7B-Instruct, produced with Heretic v1.4.0 (directional ablation / "abliteration"). Refusal behavior is suppressed via targeted weight edits to the attention output and MLP down-projections rather than fine-tuning, so the base model's knowledge and instruction-following are left largely intact.

Who this is for: developers who want a 7B coding model without the refusal guardrails - for local coding agents, pair-programming, and repo-level assistance that answers directly. Not a capability upgrade over base Qwen2.5-Coder-7B-Instruct - same model, refusal guardrails removed.

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Runs on your gaming PC

Full GGUF ladder included — pick the quant that fits your card:

Weights only, at this model's 7.6B native size; add ~1 GB for context. OOM? Drop one quant level. Headroom to spare? Go one up.

Why abliteration instead of fine-tuning

Fine-tuning a "helpful" persona on top of RLHF'd refusals fights the base model's training and tends to degrade coherence. Abliteration instead finds and edits the specific weight directions responsible for refusal, leaving the rest of the network (and its capabilities) untouched. See the Heretic repo and the original abliteration writeup for the mechanism.

Abliteration parameters

Performance

KL divergence of 0.02 on the output distribution is exceptionally low - the edit is extremely narrow. Refusals dropped from 100 to 3 out of 100 while preserving the base model's coding capability.

Made with ❤️ by RACER IS OP — follow for more uncensored models

Files

Safetensors (BF16)

The full-precision weights are in model-0000N-of-0000N.safetensors (see the repo file listing for the exact shard count and sizes).

GGUF quantizations

GGUF quantizations are published for this model (Q4_K_M, Q5_K_M, Q6_K, Q8_0). Exact sizes are in the repo file listing. Pull a specific quant with llama.cpp / ollama (see Quickstart).

Quickstart

# llama.cpp - defaults to the Q4_K_M quant if multiple are present
llama serve -hf saidutta69/Qwen2.5-Coder-7B-Instruct-heretic:Q4_K_M

# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "saidutta69/Qwen2.5-Coder-7B-Instruct-heretic"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = [{"role": "user", "content": "Who are you?"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=True,
                                        return_dict=True, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Also runnable via Ollama, LM Studio, Jan, vLLM, SGLang - see the "Use this model" widget above for copy-paste commands.

Responsible use

Refusal suppression is deliberate and works as intended: this model will comply with requests the base model would refuse, including some it shouldn't. There is no safety filtering layered on top. You are responsible for how you deploy it - don't put this behind an unmoderated public-facing endpoint serving third parties. It inherits Qwen/Qwen2.5-Coder-7B-Instruct's factual limitations and biases; abliteration removes refusal directions, it doesn't add capability or judgment.

License

Inherits the qwen-research license from the base model - research use, see the linked license for commercial terms.

Related

• Qwen2.5-Coder-3B-Instruct-heretic
• RACER IS OP — Heretic Models — full collection
• Qwen2.5-Coder-14B-Instruct-heretic

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Base model: Qwen2.5-Coder-7B-Instruct

<details> <summary>Original Qwen2.5-Coder-7B-Instruct model card (click to expand)</summary>

See the base model card at Qwen/Qwen2.5-Coder-7B-Instruct for the original architecture, training details, requirements, and citation.

</details>