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Model: Wlc7758/Deepseek-R1-Distill-Qwen-32b-uncensored
Source: Original Platform

README.md

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+---
+license: other
+license_name: deepseek
+license_link: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B/blob/main/LICENSE
+language:
+  - en
+pipeline_tag: text-generation
+base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
+tags:
+  - qwen2
+  - deepseek
+  - reasoning
+  - uncensored
+  - abliterated
+  - chain-of-thought
+library_name: transformers
+---
+
+# DeepSeek-R1-Distill-Qwen-32B Uncensored
+
+An **abliterated** (uncensored) version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) — a 32B reasoning model with chain-of-thought capabilities, minus the safety refusals.
+
+This combines DeepSeek-R1's strong reasoning with unrestricted output, making it useful for research requiring step-by-step analysis without artificial limitations.
+
+## Quick Start
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "richardyoung/Deepseek-R1-Distill-Qwen-32b-uncensored"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+messages = [{"role": "user", "content": "Walk me through how RSA encryption works, step by step."}]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
+outputs = model.generate(inputs, max_new_tokens=1024)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Model Details
+
+- **Base model**: DeepSeek-R1-Distill-Qwen-32B (32 billion parameters)
+- **Technique**: Abliteration — surgical removal of the refusal direction
+- **Architecture**: Qwen2 (decoder-only transformer)
+- **Context length**: 32,768 tokens
+- **Key strength**: Chain-of-thought reasoning without safety guardrails
+
+## Why This Model?
+
+DeepSeek-R1 is one of the strongest open-source reasoning models. The distilled 32B version retains impressive chain-of-thought capabilities at a manageable size. Abliteration allows researchers to study the full range of the model's reasoning abilities without refusal interventions.
+
+## Intended Use
+
+Research on reasoning, alignment studies, education, and creative applications requiring step-by-step analysis.
+
+## Other Models by richardyoung
+
+- **Abliterated/Uncensored models**: [Qwen2.5-7B](https://hf.co/richardyoung/Qwen2.5-7B-Instruct-abliterated-GGUF) | [Qwen3-14B](https://hf.co/richardyoung/Qwen3-14B-abliterated-GGUF) | [DeepSeek-R1-32B](https://hf.co/richardyoung/Deepseek-R1-Distill-Qwen-32b-uncensored) | [Qwen3-8B](https://hf.co/richardyoung/Qwen3-8B-Abliterated)
+- **MLX quantizations (Apple Silicon)**: [Kimi-K2 series](https://hf.co/richardyoung/Kimi-K2-Instruct-0905-MLX-4bit) | [olmOCR MLX](https://hf.co/richardyoung/olmOCR-2-7B-1025-MLX-4bit)
+- **OCR & Vision**: [olmOCR GGUF](https://hf.co/richardyoung/olmOCR-2-7B-1025-GGUF)
+- **Healthcare/Medical**: [Synthea 575K patients dataset](https://hf.co/datasets/richardyoung/synthea-575k-patients) | [CardioEmbed](https://hf.co/richardyoung/CardioEmbed)
+- **Research**: [LLM Instruction-Following Evaluation](https://hf.co/richardyoung/llm-instruction-following-paper) (arxiv:2510.18892)