DeepSeek-R1-Distill-Qwen-1.5B-heretic
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) | Q5_K_M | 1.29 GB |
| CPU-only / Apple Silicon | Q4_K_M | 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.
Performance
| Metric | This model | DeepSeek-R1-Distill-Qwen-1.5B (base) |
|---|---|---|
| Refusals (out of 100 adversarial prompts) | 3 | 18 |
| KL divergence from base | 0.0044 | 0 (by definition) |
Reproduced with Heretic 1.4.0 (directional ablation), trial 152 of 200, RNG seed 1640365500. Full reproduction assets are in reproduce/.
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 Q3_K_S | 821 MB |
DeepSeek-R1-Distill-Qwen-1.5B-heretic-Q3_K_M.gguf |
GGUF Q3_K_M | 882 MB |
DeepSeek-R1-Distill-Qwen-1.5B-heretic-Q3_K_L.gguf |
GGUF Q3_K_L | 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 Q4_K_S | 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 Q4_K_M | 1.04 GB |
DeepSeek-R1-Distill-Qwen-1.5B-heretic-Q5_K_S.gguf |
GGUF Q5_K_S | 1.17 GB |
DeepSeek-R1-Distill-Qwen-1.5B-heretic-Q5_K_M.gguf |
GGUF Q5_K_M | 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
# llama.cpp - defaults to the Q4_K_M quant
llama serve -hf saidutta69/DeepSeek-R1-Distill-Qwen-1.5B-heretic:Q4_K_M
# 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.
FAQ
What is DeepSeek-R1-Distill-Qwen-1.5B-heretic?
DeepSeek-R1-Distill-Qwen-1.5B-heretic is a decensored build of deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B produced with Heretic 1.4.0 (directional ablation). It is the same weights with the refusal direction removed, not a new fine-tune, so its capabilities track the base model.
Is DeepSeek-R1-Distill-Qwen-1.5B-heretic actually uncensored?
Yes. On Heretic's 100-prompt refusal harness it drops from 18/100 to 3/100 at KL divergence 0.0044 from the base model. These are measured values, not a claim; reproduction data is in reproduce/.
How do I run DeepSeek-R1-Distill-Qwen-1.5B-heretic locally?
llama serve -hf saidutta69/DeepSeek-R1-Distill-Qwen-1.5B-heretic:Q4_K_M pulls the default quant; the repo ships 15 GGUF quants (see Files).
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 (saidutta69) — follow for more uncensored models
License
Inherits the MIT license from the base model.
Related
deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B- the base model this checkpoint derives from- RACER IS OP - Heretic Models - full collection - every heretic model in this collection
- DeepSeek-R1-Distill-Qwen-7B-heretic - nearest DeepSeek-R1-Distill-Qwen sibling
- DeepSeek-R1-Distill-Qwen-14B-heretic - nearest DeepSeek-R1-Distill-Qwen sibling
Abliteration hardware
Recorded in reproduce/reproduce.json:
- GPU: NVIDIA GeForce RTX 4060 (8 GB VRAM)
- CPU: Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz
- OS:
Windows-11-10.0.26200-SP0 - CUDA / driver: CUDA 12.6, driver 610.62
- Software: PyTorch 2.12.1+cu126, Heretic 1.4.0
Heretic notes that KL-divergence and refusal numbers can vary slightly with hardware and library versions.