ollama run hf.co/ThalisAI/GLM-4.7-Flash-heretic:Q8_0
ollama run hf.co/ThalisAI/GLM-4.7-Flash-heretic:Q6_K
ollama run hf.co/ThalisAI/GLM-4.7-Flash-heretic:Q4_K_M

bf16 Weights

The full bf16 abliterated weights are available in the bf16/ subdirectory of this repository.

Usage with Transformers

The bf16 weights in the bf16/ subdirectory can be loaded directly with Transformers:

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ThalisAI/GLM-4.7-Flash-heretic"
tokenizer = AutoTokenizer.from_pretrained(model_id, subfolder="bf16")
model = AutoModelForCausalLM.from_pretrained(
    model_id, subfolder="bf16", torch_dtype="auto", device_map="auto"
)

messages = [{"role": "user", "content": "Hello!"}]
text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

About

This model was processed by the Apostate automated abliteration pipeline:

The source model was loaded in bf16
Heretic optimization-based abliteration was applied to remove refusal behavior
The merged model was converted to GGUF format using llama.cpp
Multiple quantization levels were generated

The abliteration process uses directional ablation to remove the model refusal directions while minimizing KL divergence from the original model behavior on harmless prompts.