saidutta69/Qwen2.5-7B-Instruct-1M-heretic

Qwen2.5-7B-Instruct-1M-heretic

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A decensored variant of Qwen/Qwen2.5-7B-Instruct-1M, produced with Heretic v1.4.0 (directional ablation / "abliteration"). a 7B instruction-tuned model from Qwen 2.5 family with 1M token context — long-context uncensored reasoning. 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 capabilities are left largely intact.

Who this is for: developers who want a 7B model with 1M token context that answers directly instead of refusing — for long-document analysis, codebase-wide reasoning, or any use case needing massive context without censorship. Best run on an 8-12 GB GPU via Q4KM GGUF.

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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.

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

Files

GGUF quants are produced with llama.cpp. Run llama serve -hf saidutta69/Qwen2.5-7B-Instruct-1M-heretic to pull the default quant.

Quickstart

# llama.cpp
llama serve -hf saidutta69/Qwen2.5-7B-Instruct-1M-heretic

# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "saidutta69/Qwen2.5-7B-Instruct-1M-heretic"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [{"role": "user", "content": "Write a Python function to merge two sorted lists."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
print(tokenizer.decode(outputs[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.

License

Inherits the other license from the base model.