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saidutta69
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Model Details
Model Provider
saidutta69
Model Tree
microsoft/phi-4
Input Modalities
Output Modalities
Supported Functionality
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.
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| File | Format | Size |
|---|---|---|
model-00001-of-00007.safetensors ... model-00007-of-00007.safetensors | BF16 | ~28 GB |
phi-4-heretic-fp16.gguf | GGUF, FP16 (base) | ~28 GB |
phi-4-heretic-Q4_K_M.gguf | GGUF, Q4_K_M | ~8.5 GB |
phi-4-heretic-Q5_K_M.gguf | GGUF, Q5_K_M | ⏳ Coming soon |
phi-4-heretic-Q6_K.gguf | GGUF, Q6_K | ⏳ Coming soon |
phi-4-heretic-Q8_0.gguf | GGUF, Q8_0 | ⏳ Coming soon |
GGUF quants are produced with llama.cpp. Run llama serve -hf saidutta69/phi-4-heretic to pull the default quant.
bash
# llama.cppllama serve -hf saidutta69/phi-4-hereticpython
# transformersfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "saidutta69/phi-4-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.
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.
Inherits the MIT license from the base model.
# llama.cppllama serve -hf saidutta69/phi-4-heretic# transformersfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "saidutta69/phi-4-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))