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+MiniCPM5-1B-heretic-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+MiniCPM5-1B-heretic-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -1,36 +1,47 @@
 ---
 license: apache-2.0
+license_link: https://huggingface.co/openbmb/MiniCPM5-1B/blob/main/LICENSE
 language:
 - en
 - zh
 library_name: transformers
 pipeline_tag: text-generation
+base_model: openbmb/MiniCPM5-1B
 tags:
 - minicpm
 - minicpm5
 - llama
-- text-generation
-- long-context
-- tool-calling
-- on-device
-- edge-ai
 - heretic
 - uncensored
 - decensored
 - abliterated
 - reproducible
+- conversational
+- text-generation-inference
+- long-context
+- tool-calling
+- on-device
+- edge-ai
 datasets:
 - openbmb/Ultra-FineWeb
 - openbmb/Ultra-FineWeb-L3
 - openbmb/UltraData-Math
 - openbmb/UltraData-SFT-2605
 ---
-# This is a decensored version of [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B), made using [Heretic](https://heretic-project.org) v1.4.0
+# MiniCPM5-1B-heretic
+
+A decensored variant of [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B), produced with [Heretic](https://github.com/p-e-w/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 MiniCPM5-1B's 1B-class SOTA capabilities — agentic tool use, code generation, 128K long-context, and hybrid Think / No-Think reasoning — without the refusal guardrails. Ideal for local agents, roleplay, research on alignment/refusal mechanics, or any use case blocked by RLHF-era over-refusal. Runs comfortably on consumer GPUs and is small enough for on-device / edge deployment.
 
 > [!TIP]
 > **This model is reproducible!**
 >
-> See the [README](reproduce/README.md) in the `reproduce` directory for more information.
+> See the [README](reproduce/README.md) in the `reproduce` directory for the exact config, full parameter/metric dump, evaluation transcripts, and SHA256 checksums.
+
+## 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](https://github.com/p-e-w/heretic) and the [original abliteration writeup](https://huggingface.co/blog/mlabonne/abliteration) for the mechanism.
 
 ## Abliteration parameters
 
@@ -53,8 +64,80 @@ datasets:
 | **KL divergence** | 0.0381 | 0 *(by definition)* |
 | **Refusals** | 2/100 | 96/100 |
 
------
+KL divergence of 0.0381 is very low for a 1B model — the edit is narrow and targeted rather than a broad perturbation. Refusals dropped from 96 to 2 out of 100 adversarial prompts while preserving MiniCPM5-1B's tool-use, code, and reasoning abilities.
+
+> Made with ❤️ by **RACER IS OP** — follow for more uncensored models
+
+## Files
+
+| File | Format | Size |
+|---|---|---|
+| `model.safetensors` | BF16 | ~2.2 GB |
+| `MiniCPM5-1B-heretic-Q8_0.gguf` | GGUF, Q8_0 | 1.10 GB |
+| `MiniCPM5-1B-heretic-Q5_K_M.gguf` | GGUF, Q5_K_M | 751 MB |
+| `MiniCPM5-1B-heretic-Q4_K_M.gguf` | GGUF, Q4_K_M | 656 MB |
+| `reproduce/` | Config + eval transcripts + checksums | — |
+
+GGUF quants are produced with [llama.cpp](https://github.com/ggml-org/llama.cpp) (MiniCPM5 uses the standard `LlamaForCausalLM` architecture, so it loads in llama.cpp / Ollama / LM Studio / Jan directly). Run `llama serve -hf saidutta69/MiniCPM5-1B-heretic` to pull the default quant.
+
+## Quickstart
+
+```bash
+# llama.cpp
+llama serve -hf saidutta69/MiniCPM5-1B-heretic
+```
+
+```python
+# transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "saidutta69/MiniCPM5-1B-heretic"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
+
+messages = [{"role": "user", "content": "Who are you? Please briefly introduce yourself."}]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    enable_thinking=False,   # set True for Think mode
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
 
+outputs = model.generate(**inputs, max_new_tokens=128)
+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. For tool/function calling, **SGLang** is the recommended backend; MiniCPM5-1B emits XML-style tool calls that SGLang's built-in `minicpm5` parser converts to OpenAI-compatible `tool_calls`.
+
+## 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 MiniCPM5-1B's factual limitations and biases; abliteration removes refusal directions, it doesn't add capability or judgment.
+
+## License
+
+Inherits the [Apache 2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) license from the base model.
+
+## Related
+
+- [Qwen2.5-0.5B-Instruct-heretic](https://huggingface.co/saidutta69/Qwen2.5-0.5B-Instruct-heretic)
+- [Qwen2.5-1.5B-Instruct-heretic](https://huggingface.co/saidutta69/Qwen2.5-1.5B-Instruct-heretic)
+- [Qwen2.5-3B-Instruct-heretic](https://huggingface.co/saidutta69/Qwen2.5-3B-Instruct-heretic)
+- [Qwen2.5-Coder-3B-Instruct-heretic](https://huggingface.co/saidutta69/Qwen2.5-Coder-3B-Instruct-heretic)
+- [Qwen3-0.6B-heretic](https://huggingface.co/saidutta69/Qwen3-0.6B-heretic)
+- [Llama-3.2-1B-Instruct-heretic](https://huggingface.co/saidutta69/Llama-3.2-1B-Instruct-heretic)
+
+---
+
+# Base model: openbmb/MiniCPM5-1B
+
+<details>
+<summary>Original MiniCPM5-1B model card (click to expand)</summary>
 
 <div align="center">
 <img src="https://raw.githubusercontent.com/OpenBMB/MiniCPM/main/assets/minicpm_logo.png" width="500em" />
@@ -384,3 +467,4 @@ Please cite our paper if you find our work valuable:
   year={2025}
 }
 ```
+</details>