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saidutta69/DeepSeek-R1-Distill-Qwen-1.5B-heretic

sourceHugging Facemitupdated 13d agoView on Hugging Face

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Model Card

DeepSeek-R1-Distill-Qwen-1.5B-heretic

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A decensored variant of deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B, 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, reasoning traces, and instruction-following are left largely intact.

Who this is for: developers who want DeepSeek-R1's distilled reasoning without refusals - the 1.5B Qwen2.5-class core runs on CPU and consumer hardware, with chain-of-thought style reasoning at ~1 GB quantized. Not a capability upgrade over base DeepSeek-R1-Distill-Qwen-1.5B - same model, refusal guardrails removed.

Runs on your gaming PC

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

Your GPU	Recommended quant	Weights
RTX 3060 / 4070 / 5070 (12 GB)	Q8_0	1.89 GB
RTX 4060 / 3070 (8 GB)	Q6_K	1.46 GB
GTX 1660 Super / 2060 / 3050 laptop (6 GB)	Q5KM	1.29 GB
CPU-only / Apple Silicon	Q4KM	1.12 GB, fits in system RAM

Weights only, at this model's ~1.8B 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.

Files

GGUF quantizations

Full quantization set (14 quants + F16) produced with llama.cpp.

File	Format	Size
`DeepSeek-R1-Distill-Qwen-1.5B-heretic-F16.gguf`	GGUF F16	3.32 GB
`DeepSeek-R1-Distill-Qwen-1.5B-heretic-Q2_K.gguf`	GGUF Q2_K	718 MB
`DeepSeek-R1-Distill-Qwen-1.5B-heretic-IQ3_S.gguf`	GGUF IQ3_S	823 MB
`DeepSeek-R1-Distill-Qwen-1.5B-heretic-Q3_K_S.gguf`	GGUF Q3KS	821 MB
`DeepSeek-R1-Distill-Qwen-1.5B-heretic-Q3_K_M.gguf`	GGUF Q3KM	882 MB
`DeepSeek-R1-Distill-Qwen-1.5B-heretic-Q3_K_L.gguf`	GGUF Q3KL	935 MB
`DeepSeek-R1-Distill-Qwen-1.5B-heretic-IQ4_XS.gguf`	GGUF IQ4_XS	979 MB
`DeepSeek-R1-Distill-Qwen-1.5B-heretic-Q4_K_S.gguf`	GGUF Q4KS	1022 MB
`DeepSeek-R1-Distill-Qwen-1.5B-heretic-Q4_0.gguf`	GGUF Q4_0	1017 MB
`DeepSeek-R1-Distill-Qwen-1.5B-heretic-Q4_1.gguf`	GGUF Q4_1	1.08 GB
`DeepSeek-R1-Distill-Qwen-1.5B-heretic-Q4_K_M.gguf`	GGUF Q4KM	1.04 GB
`DeepSeek-R1-Distill-Qwen-1.5B-heretic-Q5_K_S.gguf`	GGUF Q5KS	1.17 GB
`DeepSeek-R1-Distill-Qwen-1.5B-heretic-Q5_K_M.gguf`	GGUF Q5KM	1.20 GB
`DeepSeek-R1-Distill-Qwen-1.5B-heretic-Q6_K.gguf`	GGUF Q6_K	1.36 GB
`DeepSeek-R1-Distill-Qwen-1.5B-heretic-Q8_0.gguf`	GGUF Q8_0	1.76 GB

Loads natively in llama.cpp / Ollama / LM Studio / Jan.

Run llama serve -hf saidutta69/DeepSeek-R1-Distill-Qwen-1.5B-heretic to pull the default quant.

Quickstart

bash

# llama.cpp - defaults to the Q4_K_M quant
llama serve -hf saidutta69/DeepSeek-R1-Distill-Qwen-1.5B-heretic:Q4_K_M

python

# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "saidutta69/DeepSeek-R1-Distill-Qwen-1.5B-heretic"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
# ... inference code

Also runnable via Ollama, LM Studio, Jan, vLLM, SGLang.

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.

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

License

Inherits the MIT license from the base model.